Reading Multi Spectral Images

https://nbviewer.jupyter.org/github/thomasaarholt/hyperspy-demos/blob/master/2_SVD_and_BSS.ipynb

Multispectral Imagery

Images obtained with a ADC Lite - Tetracam's Lightweight ADC

I made pitures about:

Aluminum , Copper, Brass, Iron, Stainless Steel, Painted Iron

http://tetracam.com/Products-ADC_Lite.htm

MRobalinho - 01-06-2019 Version 8

Add Libraries

# Add libraries
import glob, os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from PIL import Image, ImageFilter, ImageOps
from openpyxl import load_workbook

# Clear all
os.system( 'cls' )

# Verify my current folder
currDir = os.path.dirname(os.path.realpath("__file__"))
mypath = currDir
print(currDir)  

C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook

# Path to the image files
folder = "imagedata06"

# Part name of file to filter files
end_file = ".tif"

# Upper End File
#end_file = end_file.upper()
path = currDir + "/" + folder + "/"
end_file

'.tif'

Read images from folder

# Read files from folder
print(path)
print('-')
print(' ---- IMAGES ON THE FOLDER :', folder, '------- *', end_file)

list_of_images = list()  # save all images on folder for further processing 

for file in os.listdir(path):
    if file.endswith(end_file):
        print(os.path.join(file))
        list_of_images.append(file)   # save all images on folder for further processing 
print('-')        

C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/
-
 ---- IMAGES ON THE FOLDER : imagedata06 ------- * .tif
Aluminum_1.tif
Aluminum_2.tif
Aluminum_3.tif
Aluminum_4.tif
Aluminum_5.tif
Aluminum_6.tif
Brass_1.tif
Brass_2.tif
Brass_3.tif
Brass_4.tif
Brass_5.tif
Brass_6.tif
CopperWire_1.tif
CopperWire_2.tif
CopperWire_3.tif
CopperWire_4.tif
CopperWire_5.tif
CopperWire_6.tif
CopperWire_7.tif
CopperWire_8.tif
Copper_1.tif
Copper_2.tif
Copper_3.tif
Copper_4.tif
Iron_1.tif
Iron_2.tif
Iron_3.tif
Iron_4.tif
PaintedIron_1.tif
PaintedIron_2.tif
PaintedIron_3.tif
PaintedIron_4.tif
PaintedIron_5.tif
PaintedIron_6.tif
PaintedIron_7.tif
StainlessSteel_1.tif
StainlessSteel_2.tif
StainlessSteel_3.tif
StainlessSteel_4.tif
StainlessSteel_5.tif
StainlessSteel_6.tif
StainlessSteel_7.tif
StainlessSteel_8.tif
StainlessSteel_9.tif
-

# Create Data Frame with image information
df_image = []

Functions to the work

# Read image with PIL
from PIL import Image, ImageFilter, ImageOps
def read_pil_image(file1):
    #print('Reading PIL image:', file1)
    try:
        im_pil = Image.open(file1)
    except:
        print("-->Unable to load image",file1)
    return im_pil

# Read image with OPENCV
import cv2
def read_cv2_image(file1):
    #print('Reading CV image:',file1)
    try:
        im_cv = cv2.imread(file1)
    except:
        print("-->Unable to load image",file1)
    return im_cv

# Look from an chanel from then image

def channel(img, n):
    """Isolate the nth channel from the image.

       n = 0: red, 1: green, 2: blue
    """
    a = np.array(img)
    a[:,:,(n!=0, n!=1, n!=2)] *= 0
#   a[:,:,n] *= 0
#   print(Image.fromarray(a), 'Get Channel n: ', n)

    print('Get Channel n: ', n)
    return Image.fromarray(a)

# def to resize 
# Given parameters : image , number to divide (resize)
def imageResize(img, n):
    width, height = img.size 

    print('Original size:', width, '/', height, 'Resize:',n)
    
    newWidth = int(width / n)
    newHeight = int(height / n)
    img.resize((newWidth, newHeight), Image.ANTIALIAS)
    print('New size:', newWidth, '/', newHeight)
    return img

# Obtain main color from image
# https://convertingcolors.com/rgb-color-169_171_170.html
    
def get_main_color(path, file):
    #img = Image.open(path+file)
    file1 = path+file
    # Read image
    img = read_pil_image(file1)
    if img == None:
        print("-->Unable to load image",file1)
        
    colors = img.getcolors( 1024*1024) #put a higher value if there are many colors in your image
    print('Get main Color file:', file)
    max_occurence, most_present = 0, 0
    try:
        for c in colors:
            if c[0] > max_occurence:
                (max_occurence, most_present) = c
        return most_present
    except TypeError:
        raise Exception("Too many colors in the image")

#!/usr/bin/python

# Return one 24-bit color value 
def rgbToDecimal(x_rgb):
    r,g,b = rgbToRGB(x_rgb)
    rgb_dec = (r << 16) + (g << 8) + b
    #print('RGB Color:', x_rgb, '   Dec:', rgb_dec)
    return rgb_dec

# Convert 24-bit color value to RGB
def colorToRGB(c):
    r = c >> 16
    c -= r * 65536;
    g = c / 256
    c -= g * 256;
    b = c
    return [r, g, b]

def rgbToRGB(x_rgb):
    x_rgb = list(x_rgb)
    r = x_rgb[0]
    g = x_rgb[1]
    b = x_rgb[2]

    #print('rgbToRGB:',x_rgb, r,g,b)
    return r, g, b

def getRGBfromI(RGBint):
    blue =  RGBint & 255
    green = (RGBint >> 8) & 255
    red =   (RGBint >> 16) & 255
    return red, green, blue

def getIfromRGB(rgb):
    red = rgb[0]
    green = rgb[1]
    blue = rgb[2]
    #print('getIfromRGB:', red, green, blue)
    RGBint = (red<<16) + (green<<8) + blue
    return RGBint

# RGB to Hex Decimal
def rgb_to_hex(rgb):
    rgb_int = bytes(rgb).hex()
    rgb_dec = '#'+str(rgb_int)
    #print('RGB :',rgb, '  Hex Dec:', rgb_dec)
    return rgb_dec

# Test
#x_rgb = (254, 250, 255)
#rgb_hex = rgb_to_hex(x_rgb)
#rgb_dec = rgbToDecimal(x_rgb)

# https://github.com/conda-forge/webcolors-feedstock
# conda config --add channels conda-forge
# conda install webcolors
# It is possible to list all of the versions of webcolors available on your platform with:
#       conda search webcolors --channel conda-forge

# COLOR NAME
import webcolors
def get_color_name(rgb_x):
    min_colours = {}
    for key, name in webcolors.css21_hex_to_names.items():
        r_c, g_c, b_c = webcolors.hex_to_rgb(key)
        rd = (r_c - rgb_x[0]) ** 2
        gd = (g_c - rgb_x[1]) ** 2
        bd = (b_c - rgb_x[2]) ** 2
        min_colours[(rd + gd + bd)] = name
    print('Color name from RGB:',rgb_x,'  is :',min_colours[min(min_colours.keys())])
    return min_colours[min(min_colours.keys())]

# Get color name from RGB
# https://stackoverflow.com/questions/2453344/find-the-colour-name-from-a-hexadecimal-colour-code

colorof = {'#F0F8FF':"aliceblue",
'#FAEBD7':"antiquewhite",
'#00FFFF':"aqua",
'#7FFFD4':"aquamarine",
'#F0FFFF':"azure",
'#F5F5DC':"beige",
'#FFE4C4':"bisque",
'#000000':"black",
'#FFEBCD':"blanchedalmond",
'#0000FF':"blue",
'#8A2BE2':"blueviolet",
'#A52A2A':"brown",
'#DEB887':"burlywood",
'#5F9EA0':"cadetblue",
'#7FFF00':"chartreuse",
'#D2691E':"chocolate",
'#FF7F50':"coral",
'#6495ED':"cornflowerblue",
'#FFF8DC':"cornsilk",
'#DC143C':"crimson",
'#00FFFF':"cyan",
'#00008B':"darkblue",
'#008B8B':"darkcyan",
'#B8860B':"darkgoldenrod",
'#A9A9A9':"darkgray",
'#006400':"darkgreen",
'#BDB76B':"darkkhaki",
'#8B008B':"darkmagenta",
'#556B2F':"darkolivegreen",
'#FF8C00':"darkorange",
'#9932CC':"darkorchid",
'#8B0000':"darkred",
'#E9967A':"darksalmon",
'#8FBC8B':"darkseagreen",
'#483D8B':"darkslateblue",
'#2F4F4F':"darkslategray",
'#00CED1':"darkturquoise",
'#9400D3':"darkviolet",
'#FF1493':"deeppink",
'#00BFFF':"deepskyblue",
'#696969':"dimgray",
'#1E90FF':"dodgerblue",
'#B22222':"firebrick",
'#FFFAF0':"floralwhite",
'#228B22':"forestgreen",
'#FF00FF':"fuchsia",
'#DCDCDC':"gainsboro",
'#F8F8FF':"ghostwhite",
'#FFD700':"gold",
'#DAA520':"goldenrod",
'#808080':"gray",
'#008000':"green",
'#ADFF2F':"greenyellow",
'#F0FFF0':"honeydew",
'#FF69B4':"hotpink",
'#CD5C5C':"indianred",
'#4B0082':"indigo",
'#FFFFF0':"ivory",
'#F0E68C':"khaki",
'#E6E6FA':"lavender",
'#FFF0F5':"lavenderblush",
'#7CFC00':"lawngreen",
'#FFFACD':"lemonchiffon",
'#ADD8E6':"lightblue",
'#F08080':"lightcoral",
'#E0FFFF':"lightcyan",
'#FAFAD2':"lightgoldenrodyellow",
'#D3D3D3':"lightgray",
'#90EE90':"lightgreen",
'#FFB6C1':"lightpink",
'#FFA07A':"lightsalmon",
'#20B2AA':"lightseagreen",
'#87CEFA':"lightskyblue",
'#778899':"lightslategray",
'#B0C4DE':"lightsteelblue",
'#FFFFE0':"lightyellow",
'#00FF00':"lime",
'#32CD32':"limegreen",
'#FAF0E6':"linen",
'#FF00FF':"magenta",
'#800000':"maroon",
'#66CDAA':"mediumaquamarine",
'#0000CD':"mediumblue",
'#BA55D3':"mediumorchid",
'#9370DB':"mediumpurple",
'#3CB371':"mediumseagreen",
'#7B68EE':"mediumslateblue",
'#00FA9A':"mediumspringgreen",
'#48D1CC':"mediumturquoise",
'#C71585':"mediumvioletred",
'#191970':"midnightblue",
'#F5FFFA':"mintcream",
'#FFE4E1':"mistyrose",
'#FFE4B5':"moccasin",
'#FFDEAD':"navajowhite",
'#000080':"navy",
'#FDF5E6':"oldlace",
'#808000':"olive",
'#6B8E23':"olivedrab",
'#FFA500':"orange",
'#FF4500':"orangered",
'#DA70D6':"orchid",
'#EEE8AA':"palegoldenrod",
'#98FB98':"palegreen",
'#AFEEEE':"paleturquoise",
'#DB7093':"palevioletred",
'#FFEFD5':"papayawhip",
'#FFDAB9':"peachpuff",
'#CD853F':"peru",
'#FFC0CB':"pink",
'#DDA0DD':"plum",
'#B0E0E6':"powderblue",
'#800080':"purple",
'#FF0000':"red",
'#BC8F8F':"rosybrown",
'#4169E1':"royalblue",
'#8B4513':"saddlebrown",
'#FA8072':"salmon",
'#F4A460':"sandybrown",
'#2E8B57':"seagreen",
'#FFF5EE':"seashell",
'#A0522D':"sienna",
'#C0C0C0':"silver",
'#87CEEB':"skyblue",
'#6A5ACD':"slateblue",
'#708090':"slategray",
'#FFFAFA':"snow",
'#00FF7F':"springgreen",
'#4682B4':"steelblue",
'#D2B48C':"tan",
'#008080':"teal",
'#D8BFD8':"thistle",
'#FF6347':"tomato",
'#40E0D0':"turquoise",
'#EE82EE':"violet",
'#F5DEB3':"wheat",
'#FFFFFF':"white",
'#F5F5F5':"whitesmoke",
'#FFFF00':"yellow",
'#9ACD32':"yellowgreen"}


def get_rgb_color_name(rgb):
    
    hex_from_rgb = rgb_to_hex(rgb)  # transform RGB into hexadecimal
    hx = hex_from_rgb[1:8]
    #print(hx)
    # if color is found in dict
    if colorof.get(hx):return colorof[hx]

    # else return its closest available color
    m = 16777215
    k = '000000'
    for key in colorof.keys():
        key_color = key[1:8]
        #print(key_color)
        a = int(hx[:2],16)-int(key_color[:2],16)
        b = int(hx[2:4],16)-int(key_color[2:4],16)
        c = int(hx[4:],16)-int(key_color[4:],16)

        v = a*a+b*b+c*c # simple measure for distance between colors

        # v = (r1 - r2)^2 + (g1 - g2)^2 + (b1 - b2)^2

        if v <= m:
            m = v
            k = key

    return colorof[k], hex_from_rgb

# Test
#rgb_1 = (216, 220, 223)
#cname, hexdc = get_rgb_color_name(rgb_1)
#print('Found:',    cname, '  Hex:', hexdc)     # found in dict

# Increase the contrast image
# im - image
# xvalue = contrast value
# https://pillow.readthedocs.io/en/4.0.x/reference/ImageEnhance.html
from PIL import ImageEnhance
# Path + file name + numeric value to enhancement

def contrast(path, xfile, xvalue):
    print('   Enhance image:', xfile, '  Value:', xvalue)
    file1 = path + xfile
    # Read Image
    im = read_pil_image(file1)
    if im == None:
        print("-->Unable to load image",file1)
    
    enh = ImageEnhance.Contrast(im)
    # enh.enhance(1.0).show("30% more contrast")
    x_enh = enh.enhance(xvalue)
     # Create name file masked
    f2_file = 'Enh_' + xfile
    print('   Save enhanced file :', f2_file)
    x_enh.save(f2_file)  # save enhanced file
    return x_enh, f2_file

# Return RGB separately
def return_rgb_from_RGB(rgb):
    p_rgb = list(rgb)
    red   = p_rgb[0]
    green = p_rgb[1]
    blue  = p_rgb[2]
    return red, green, blue

# Return distance from 2 colors

# http://hanzratech.in/2015/01/16/color-difference-between-2-colors-using-python.html
# https://python-colormath.readthedocs.io/en/latest/delta_e.html#delta-e-cie-2000

from colormath.color_objects import sRGBColor, LabColor
from colormath.color_conversions import convert_color
from colormath.color_diff import delta_e_cie2000

def delta_2_colors(rgb_1, rgb_2):
    #print('   Delta colors: ', rgb_1, rgb_2)
    #---- first color
    xr, xg, xb = return_rgb_from_RGB(rgb_1)
    # Red Color
    color1_rgb = sRGBColor(xr, xg, xb)

    #--- other color
    rgb_1 = rgb_2
    xr, xg, xb = return_rgb_from_RGB(rgb_1)
    # Blue Color
    color2_rgb = sRGBColor(xr, xg, xb)

    # Convert from RGB to Lab Color Space
    color1_lab = convert_color(color1_rgb, LabColor)

    # Convert from RGB to Lab Color Space
    color2_lab = convert_color(color2_rgb, LabColor)

    # Find the color difference
    delta_e = delta_e_cie2000(color1_lab, color2_lab)

    #print("      The difference between the 2 color = ", delta_e)
    return delta_e

# Remove Background - Put red background
#https://stackoverflow.com/questions/29313667/how-do-i-remove-the-background-from-this-kind-of-image
    
import cv2
import numpy as np

def red_background(path, xfile):
    print('   Red background for image:', xfile)
    #== Parameters =======================================================================
    BLUR = 21
    CANNY_THRESH_1 = 10
    CANNY_THRESH_2 = 100
    MASK_DILATE_ITER = 10
    MASK_ERODE_ITER = 10
    MASK_COLOR = (0.0,0.0,1.0) # In BGR format

    #== Processing =======================================================================
    file1 = path + xfile
    #-- Read image -----------------------------------------------------------------------
    #img = cv2.imread(file1)
    # Read image
    img = read_cv2_image(file1)
    if img.any() == None:
        print("-->Unable to load image",file1)
    
    # Create GRAY Image
    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

    #-- Edge detection -------------------------------------------------------------------
    edges = cv2.Canny(gray, CANNY_THRESH_1, CANNY_THRESH_2)
    edges = cv2.dilate(edges, None)
    edges = cv2.erode(edges, None)

    #-- Find contours in edges, sort by area ---------------------------------------------
    contour_info = []
    _, contours, _ = cv2.findContours(edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
    for c in contours:
        contour_info.append((
            c,
            cv2.isContourConvex(c),
            cv2.contourArea(c),
        ))
    contour_info = sorted(contour_info, key=lambda c: c[2], reverse=True)
    max_contour = contour_info[0]

    #-- Create empty mask, draw filled polygon on it corresponding to largest contour ----
    # Mask is black, polygon is white
    mask = np.zeros(edges.shape)
    for c in contour_info:
        cv2.fillConvexPoly(mask, c[0], (255))

    #-- Smooth mask, then blur it
    mask = cv2.dilate(mask, None, iterations=MASK_DILATE_ITER)
    mask = cv2.erode(mask, None, iterations=MASK_ERODE_ITER)
    mask = cv2.GaussianBlur(mask, (BLUR, BLUR), 0)
    mask_stack = np.dstack([mask]*3)    # Create 3-channel alpha mask

    #-- Blend masked img into MASK_COLOR background
    mask_stack  = mask_stack.astype('float32') / 255.0         
    img         = img.astype('float32') / 255.0    
    masked = (mask_stack * img) + ((1-mask_stack) * MASK_COLOR)  
    masked = (masked * 255).astype('uint8')                    
    
    cv2.imwrite(path+"MASK_"+xfile,masked)
    
    # Create name file masked
    f2_file = 'Mask_'+ xfile
    file2 = path + f2_file
    
    # Write masked image on disk
    print('   Save masked image with red background:', f2_file)
    cv2.imwrite(file2, masked)           # Save
    # Return name file masked and image masked
    return f2_file, masked

# Test
'''
xfile = 'Brass_001.tif'
f2_file, masked = red_background(path,xfile)
%matplotlib inline
plt.imshow(masked)
plt.title('Remove image background:'+xfile,fontsize=20)
plt.show()
'''

"\nxfile = 'Brass_001.tif'\nf2_file, masked = red_background(path,xfile)\n%matplotlib inline\nplt.imshow(masked)\nplt.title('Remove image background:'+xfile,fontsize=20)\nplt.show()\n"

# https://convertingcolors.com/rgb-color-169_171_170.html

# return most_present RGB, RGB, color name, list RGB colors without RED, list RGB colors without back

import collections

def get_main_color_without_red_and_floor(path, f2_file):
    print('    Main color from image:', f2_file)
    file1 = path + f2_file
    
    # Read image
    img = read_pil_image(file1)
    if img == None:
        print("-->Unable to load image",file1)
        
    colors = img.getcolors( 1024*1024) #put a higher value if there are many colors in your image
    #-----
    # Create list with colors without Background red color (near Background color)
    list_non_back = list()
    list_dec_back = list()   # List from decimal colors to list_non_back
    #
    print('...  List without excluded colors')
    # Convert list to decimal color
    for color in colors:
        # Diference between colors
       # print(color[1])
        rgb = color[1]

        excluded_rgb = False
        
        #Verify color name
        xt_color_name , hexdc = get_rgb_color_name(rgb)
        
        # Exclusion for some colors (Red Backgroud, Black foor, etc)
        if "red"   in xt_color_name: 
             excluded_rgb = True
        if "black" in xt_color_name:        
             excluded_rgb = True
        if "white" in xt_color_name:
            excluded_rgb = True
        if "cream" in xt_color_name:
            excluded_rgb = True             
        
        # Force Only for non-tif files we do not delete anything
        if file.endswith('.tif'):
            excluded_rgb = False
    
        if  excluded_rgb == True:     # Exclude COLOR  
            #print("Cor excluida", rgb, xt_color_name )
            excluded_rgb = True
        else:
            # OK COLOR - Save color in the list of correct colors (list_non_back)
            #print("Cor OK", rgb, xt_color_name )
            list_non_back.append(rgb)
            # Decimal color
            rgb_dec    = rgbToDecimal(rgb)
            list_dec_back.append(rgb_dec)   
  
    #-----
    print('Count ocurrencies for color')
    most_present = 0

    # Most common color in the list - list_non_back
    x = collections.Counter(list_non_back)
    print('      4 Most common colors:', x.most_common(4))  # Five most common colors
    most_present = x.most_common(1)
    xrgb = list_non_back[0] # common color
    
    # ----- color name --
    #xt_color_name = get_color_name(xrgb)
    print('      Read color name:', xrgb)  # Color name from RGB
    xt_color_name , hexdc = get_rgb_color_name(xrgb)
    print('      Main Color file:', f2_file, ' RGB:', most_present, xrgb, ' Color name:', xt_color_name,' Hex:',hexdc)
    
    return most_present, xrgb, xt_color_name, list_non_back, list_dec_back

# Test
#xfile = 'Copper_001.tif'
#most_present, xrgb, xt_color_name, list_non_back, \
#     list_dec_back = get_main_color_without_red_and_floor(path, xfile)

# https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_histograms/py_histogram_begins/py_histogram_begins.html
# Print histogram using Opencv
import cv2
import numpy as np
from matplotlib import pyplot as plt

def print_cv_hist(path, xfile):
    file1 = path + xfile
    print('Cv2 Hist from file:', file1)
    
    # Read image
    img_cv = read_cv2_image(file1)
    if img_cv.any() == None:
        print("-->Unable to load image",file1)

    # create a mask
    mask = np.zeros(img_cv.shape[:2], np.uint8)

    # define area to extract image from original
    #    Left:height , right:length
    mask[200:1400, 200:1800] = 255
    masked_img = cv2.bitwise_and(img_cv, img_cv ,mask = mask)

    # Calculate histogram with mask and without mask
    # Check third argument for mask
    hist_full = cv2.calcHist([img_cv],[0],None,[256],[0,256])
    hist_mask = cv2.calcHist([img_cv],[0],mask,[256],[0,256])

    plt.figure(figsize=(18,5))

    plt.subplot(141), plt.imshow(img_cv, 'gray')
    plt.title("Original")

    plt.subplot(142), plt.imshow(mask,'gray')
    plt.title('Mask')

    plt.subplot(143), plt.imshow(masked_img, 'gray')
    plt.title('Masked image')


    ax=plt.subplot(144), plt.plot(hist_full), plt.plot(hist_mask)
    ax = plt.gca()
    ax.grid(True)
    plt.title('Histogram')
    plt.xlim([0,256])

    plt.suptitle('IMAGE HISTOGRAM',fontsize=18)
    plt.xlabel('Image:'+xfile,fontsize=18)
    plt.ylabel('All chanels',fontsize=10)
    plt.savefig(path+'Hist_cv2_'+xfile)   # Save Histograme Figure
    plt.show()
    return

# Test
#xfile = 'Copper_001.tif'
#print_cv_hist(path, xfile)

# https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_histograms/py_histogram_begins/py_histogram_begins.html
# Print histogram using Opencv and matplotlib

import cv2
import numpy as np
from matplotlib import pyplot as plt

def print_matplot_hist(path, xfile):
    file1 = path + xfile   
    print('Matplot Hist from file:', file1)
    
    # Read image
    img_mp = read_cv2_image(file1)
    if img_mp.any() == None:
        print("-->Unable to load image",file1)
    
    color = ('b','g','r')
    ax = plt.figure(figsize=(10,5))
    ax = plt.gca()
    ax.grid(True)
    
    for i,col in enumerate(color):
        histr = cv2.calcHist([img_mp],[i],None,[256],[0,256])
        plt.plot(histr,color = col, label='Band '+col.upper())
        plt.xlim([0,256])

    plt.title('Histogram of the image',fontsize=20)
    plt.xlabel('Image:'+xfile,fontsize=18)
    plt.ylabel('All chanels',fontsize=18)
    plt.legend(bbox_to_anchor=(.90,0.85),bbox_transform=plt.gcf().transFigure)
    plt.savefig(path+'Hist_'+xfile)   # Save Histograme Figure
    plt.show()   
        
    return

# Test
#xfile = 'Copper_1.tif'
#print_matplot_hist(path, xfile)

# Max and Min value from Histogram and each position
#l = np.array(hist_full).tolist()  - Transform array in a list

import cv2
import numpy as np
from matplotlib import pyplot as plt

def histogram_max_min(path, xfile):

    file1 = path+xfile
    print('Histogram analisys:', file1)
    
    # Read image
    imgh = read_cv2_image(file1)
    if imgh.any() == None:
        print("-->Unable to load image",file1)

    # Calculate histogram without mask
    hist_full = cv2.calcHist([imgh],[0],None,[256],[0,256])
 
    # Transform array in a list
    hist_list = np.array(hist_full).tolist()

    # Valor maximo e minimo do Histograma e sua posição
    val_max  = max(hist_list)
    xval_max = int(val_max[0])
 
    val_avg  =  max(hist_list) 
    xval_avg = int(val_avg[0]) / len(hist_list)
    xval_avg = int(xval_avg)
    
    val_min  = min(hist_list)
    xval_min = int(val_min[0])
    
    idx_max = hist_list.index(val_max)
    idx_min = hist_list.index(val_min)
    
    #print("Valor Max Histograma:", xval_max, '  Posição do valor Max:', idx_max)
    #print("Valor Min Histograma:", xval_min, '  Posição do valor Min:', idx_min)
    #print("Valor Avg Histograma:", xval_avg)
  
    return xval_max, idx_max, xval_min, idx_min

# Test
#xfile = 'Copper_001.tif'
#_,_,_,_ = histogram_max_min(path, xfile)
 

# Read image folder
import glob, os
def get_image_folder(xfile1):
    # Path to the image files
    path = currDir + "/" + folder + "/"
    # File
    file1 = path + xfile1
    print(file1)
    
    return file1

# Obtain percentage of channels R,G,B
import matplotlib.image as mpimg
def percent_rgb(path, xfile):
    print('    RGB percent from image:', xfile)
    emptyBlue = []
    emptyGreen= []
    emptyRed= []
    
    all_path = path + xfile
    # Read file
    img = mpimg.imread(all_path)
    imgplot = plt.imshow(img)
    # Mean of the array of each chanel
    RGBtuple = np.array(img).mean(axis=(0,1))

    averageRed = RGBtuple[0]
    averageGreen = RGBtuple[1]
    averageBlue = RGBtuple[2]

    percentageGreen = averageGreen/(averageRed+averageGreen+averageBlue) * 100
    percentageBlue = averageBlue/(averageRed+averageGreen+averageBlue) * 100
    percentageRed = averageRed/(averageRed+averageGreen+averageBlue) * 100

    emptyBlue+=[percentageBlue]
    emptyGreen+=[percentageGreen]
    emptyRed+=[percentageRed]
    print('     ------------------------------')
    print('     Percent Red',percentageRed)
    print('     Percent Green',percentageGreen)
    print('     Percent Blue',percentageBlue)
    print('     ------------------------------')
    return percentageRed, percentageGreen, percentageBlue

# Print all the informations from image, and create a pandas data frame with the relevant information

def print_file(path, xfile):
    print('------------------------------------------------------------------------')   
    file1 = path + xfile
    
    # Read image
    tif_f1 = read_pil_image(file1)
    if tif_f1 == None:
        print("-->Unable to load image",file1)

    print('Inf.File:',xfile)

    # Transform Image to array
    aArray = np.array(tif_f1)
    # Array sum  
    xsum = aArray.sum() / 1000000
    
    # Get channel 0
    x0_channel = channel(tif_f1, 0)
    aArray = np.array(x0_channel)
    xsum_0 = aArray.sum() / 1000000  

    # Get channel 1
    x1_channel = channel(tif_f1, 1)
    aArray = np.array(x1_channel)
    xsum_1 = aArray.sum() / 1000000  

    # Get channel 2
    x2_channel = channel(tif_f1, 2)
    aArray = np.array(x2_channel)
    xsum_2 = aArray.sum() / 1000000  

    # Histogram from image
    aHist = tif_f1.histogram()
    hsum = sum(aHist) / 100000

    # Histogram channel 0
    aHist_0 = x0_channel.histogram()
    hsum_0 = sum(aHist_0) / 100000

    # Histogram channel 1
    aHist_1 = x1_channel.histogram()
    hsum_1 = sum(aHist_1) / 100000

    # Histogram chanel 0
    aHist_2 = x2_channel.histogram()
    hsum_2 = sum(aHist_2) / 100000

    # number elements on list
    nlist = len(aHist)
    
    # Max and Min from Histogram
    xval_max, idx_max, xval_min, idx_min = histogram_max_min(path, xfile)
    
    # Percentage RGB
    perc_R, perc_G, perc_B = percent_rgb(path, xfile)

    # Get color
    # Enhancement Contrast color for better definition
    # f1_file has the file name saved enhanced  
    xvalue = 2.0  
    print('Enhancement color:', xfile, '  Value:',xvalue)  
    x_enh, f1_file = contrast(path, xfile, xvalue)

    # Remove Background - Put red background
    # f2_file has the file name saved masked
    
    # Only red Background for NON tif files
    #xend_file = file.endswith('*.TIF').upper()
    if file.endswith('*.TIF'):
        f2_file = f1_file
        img_masked = tif_f1
    else:    
        file1 = path+f1_file  
        print('Red background:', path, f1_file)   
        f2_file, img_masked = red_background(path, f1_file)

    # Get Main Color - 
    print('Most common color:', path, f2_file)  
    
    # most present color, RGB from most present color:
    # color name , Hex from rgb , list colors withour red, list colors without back, decimal list colors without back
    most_present, xrgb, xt_color_name, list_non_back,list_dec_back = get_main_color_without_red_and_floor(path, f2_file) 
    
    # HEX fom most present color
    hex_color  = rgb_to_hex(xrgb)
    
    # Decimal from most present color
    rgb_dec    = rgbToDecimal(xrgb)
    #----
    # Get Extrems of the image
    extr_a = tif_f1.getextrema() 
    # Transform tuple in a list    
    extr_b = [x for sets in extr_a for x in sets]
    # Sum the list  
    sum_list = sum(extr_b) 
    med_extr  = sum_list / len(extr_b)
    #print('List Extremes:',extr_a,'Sum:',sum_list,'Len:', len(extr_b), 'Med:',med_extr)


    # Obtain name file without extension 
    sample_name = os.path.basename(xfile).split('_')[0]

    # Print information  
    print(sample_name,' Size:',tif_f1.size, ' Format:',tif_f1.format, ' Mode:', tif_f1.mode)
    print('          Sum array:',xsum, ' Sum Ch 0:', xsum_0, ' Sum Ch 1:', xsum_1, ' Sum Ch 2:', xsum_2)      
    print('          Histog   :',hsum ,'  N.List elem:', nlist, ' Max:', xval_max, 'Idx Max:', idx_max, '  Min:', xval_min, 'Idx Min:', idx_min )
    print('          Color    :',xt_color_name,'   RGB   :',xrgb, '   Hex color:', hex_color,'  Dec Color:',rgb_dec)
    print('          Extremes :',extr_a, 'Med Extremes:',med_extr)
    print('          Percentage R:', perc_R,'  Percentage G:', perc_B, '  Percentage B:', perc_B)

    # insert information in a Pandas Data Frame
    df_image.append((folder, xfile, sample_name, tif_f1.size, tif_f1.format, tif_f1.mode , 
                       xsum, xsum_0, xsum_1, xsum_2, hsum, nlist, xt_color_name, xrgb, hex_color, 
                       rgb_dec, med_extr, xval_max, idx_max, xval_min, idx_min,
                       perc_R, perc_G, perc_B))
    
    return most_present, xrgb, xt_color_name, list_non_back, list_dec_back
 

Starting image analysis

# Timer
import datetime
master_time_start = datetime.datetime.now() # global timer start

# Create Data Frame with image information
df_image = []

xend_file = "*" + end_file
# change work to folder path
os.chdir(path)
print('Analysing Images from:',path, xend_file)

for file in glob.glob(xend_file):
    file_time_start = datetime.datetime.now() # timer start
    list_dec_back = list() # List with decimal colors in the image
    print(file)
    most_present, xrgb, xt_color_name, list_non_back, list_dec_back = print_file(path,file)
    file_time_end = datetime.datetime.now()
    elapsed = file_time_end - file_time_start
    print('File Start:', file_time_start, ' Finish:', file_time_end, '  File Time:',elapsed) # timer end
    
master_time_end = datetime.datetime.now() # global timer end
elapsed = master_time_end - master_time_start
print('Process Start:', master_time_start, ' Finish:', master_time_end, '  Global Time:',elapsed)

Analysing Images from: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ *.tif
Aluminum_1.tif
------------------------------------------------------------------------
Inf.File: Aluminum_1.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Aluminum_1.tif
    RGB percent from image: Aluminum_1.tif
     ------------------------------
     Percent Red 33.57960394466849
     Percent Green 33.57960394466849
     Percent Blue 32.840792110663024
     ------------------------------
Enhancement color: Aluminum_1.tif   Value: 2.0
   Enhance image: Aluminum_1.tif   Value: 2.0
   Save enhanced file : Enh_Aluminum_1.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_Aluminum_1.tif
   Red background for image: Enh_Aluminum_1.tif
   Save masked image with red background: Mask_Enh_Aluminum_1.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_Aluminum_1.tif
    Main color from image: Mask_Enh_Aluminum_1.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((243, 229, 223), 1), ((239, 229, 223), 1), ((237, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_Aluminum_1.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
Aluminum  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 658.137536  Sum Ch 0: 220.999978  Sum Ch 1: 220.999978  Sum Ch 2: 216.13758
          Histog   : 94.37184   N.List elem: 768  Max: 237292 Idx Max: 11   Min: 543 Idx Min: 109
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.57960394466849   Percentage G: 32.840792110663024   Percentage B: 32.840792110663024
File Start: 2019-06-01 19:23:49.323230  Finish: 2019-06-01 19:24:01.932546   File Time: 0:00:12.609316
Aluminum_2.tif
------------------------------------------------------------------------
Inf.File: Aluminum_2.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Aluminum_2.tif
    RGB percent from image: Aluminum_2.tif
     ------------------------------
     Percent Red 33.57823383501651
     Percent Green 33.57823383501651
     Percent Blue 32.84353232996698
     ------------------------------
Enhancement color: Aluminum_2.tif   Value: 2.0
   Enhance image: Aluminum_2.tif   Value: 2.0
   Save enhanced file : Enh_Aluminum_2.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_Aluminum_2.tif
   Red background for image: Enh_Aluminum_2.tif
   Save masked image with red background: Mask_Enh_Aluminum_2.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_Aluminum_2.tif
    Main color from image: Mask_Enh_Aluminum_2.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((255, 229, 223), 1), ((247, 229, 223), 1), ((243, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_Aluminum_2.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
Aluminum  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 693.024169  Sum Ch 0: 232.705276  Sum Ch 1: 232.705276  Sum Ch 2: 227.613617
          Histog   : 94.37184   N.List elem: 768  Max: 280312 Idx Max: 252   Min: 648 Idx Min: 129
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.57823383501651   Percentage G: 32.84353232996698   Percentage B: 32.84353232996698
File Start: 2019-06-01 19:24:01.932546  Finish: 2019-06-01 19:24:15.082001   File Time: 0:00:13.149455
Aluminum_3.tif
------------------------------------------------------------------------
Inf.File: Aluminum_3.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Aluminum_3.tif
    RGB percent from image: Aluminum_3.tif
     ------------------------------
     Percent Red 33.563387602119285
     Percent Green 33.563387602119285
     Percent Blue 32.873224795761416
     ------------------------------
Enhancement color: Aluminum_3.tif   Value: 2.0
   Enhance image: Aluminum_3.tif   Value: 2.0
   Save enhanced file : Enh_Aluminum_3.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_Aluminum_3.tif
   Red background for image: Enh_Aluminum_3.tif
   Save masked image with red background: Mask_Enh_Aluminum_3.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_Aluminum_3.tif
    Main color from image: Mask_Enh_Aluminum_3.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((254, 229, 223), 1), ((250, 229, 223), 1), ((248, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_Aluminum_3.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
Aluminum  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 696.532319  Sum Ch 0: 233.779842  Sum Ch 1: 233.779842  Sum Ch 2: 228.972635
          Histog   : 94.37184   N.List elem: 768  Max: 248402 Idx Max: 8   Min: 819 Idx Min: 94
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.563387602119285   Percentage G: 32.873224795761416   Percentage B: 32.873224795761416
File Start: 2019-06-01 19:24:15.082497  Finish: 2019-06-01 19:24:41.015366   File Time: 0:00:25.932869
Aluminum_4.tif
------------------------------------------------------------------------
Inf.File: Aluminum_4.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Aluminum_4.tif
    RGB percent from image: Aluminum_4.tif
     ------------------------------
     Percent Red 33.57960394466849
     Percent Green 33.57960394466849
     Percent Blue 32.840792110663024
     ------------------------------
Enhancement color: Aluminum_4.tif   Value: 2.0
   Enhance image: Aluminum_4.tif   Value: 2.0
   Save enhanced file : Enh_Aluminum_4.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_Aluminum_4.tif
   Red background for image: Enh_Aluminum_4.tif
   Save masked image with red background: Mask_Enh_Aluminum_4.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_Aluminum_4.tif
    Main color from image: Mask_Enh_Aluminum_4.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((243, 229, 223), 1), ((239, 229, 223), 1), ((237, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_Aluminum_4.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
Aluminum  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 658.137536  Sum Ch 0: 220.999978  Sum Ch 1: 220.999978  Sum Ch 2: 216.13758
          Histog   : 94.37184   N.List elem: 768  Max: 237292 Idx Max: 11   Min: 543 Idx Min: 109
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.57960394466849   Percentage G: 32.840792110663024   Percentage B: 32.840792110663024
File Start: 2019-06-01 19:24:41.015366  Finish: 2019-06-01 19:25:05.415098   File Time: 0:00:24.399732
Aluminum_5.tif
------------------------------------------------------------------------
Inf.File: Aluminum_5.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Aluminum_5.tif
    RGB percent from image: Aluminum_5.tif
     ------------------------------
     Percent Red 33.57823383501651
     Percent Green 33.57823383501651
     Percent Blue 32.84353232996698
     ------------------------------
Enhancement color: Aluminum_5.tif   Value: 2.0
   Enhance image: Aluminum_5.tif   Value: 2.0
   Save enhanced file : Enh_Aluminum_5.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_Aluminum_5.tif
   Red background for image: Enh_Aluminum_5.tif
   Save masked image with red background: Mask_Enh_Aluminum_5.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_Aluminum_5.tif
    Main color from image: Mask_Enh_Aluminum_5.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((255, 229, 223), 1), ((247, 229, 223), 1), ((243, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_Aluminum_5.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
Aluminum  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 693.024169  Sum Ch 0: 232.705276  Sum Ch 1: 232.705276  Sum Ch 2: 227.613617
          Histog   : 94.37184   N.List elem: 768  Max: 280312 Idx Max: 252   Min: 648 Idx Min: 129
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.57823383501651   Percentage G: 32.84353232996698   Percentage B: 32.84353232996698
File Start: 2019-06-01 19:25:05.415602  Finish: 2019-06-01 19:25:24.494238   File Time: 0:00:19.078636
Aluminum_6.tif
------------------------------------------------------------------------
Inf.File: Aluminum_6.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Aluminum_6.tif
    RGB percent from image: Aluminum_6.tif
     ------------------------------
     Percent Red 33.563387602119285
     Percent Green 33.563387602119285
     Percent Blue 32.873224795761416
     ------------------------------
Enhancement color: Aluminum_6.tif   Value: 2.0
   Enhance image: Aluminum_6.tif   Value: 2.0
   Save enhanced file : Enh_Aluminum_6.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_Aluminum_6.tif
   Red background for image: Enh_Aluminum_6.tif
   Save masked image with red background: Mask_Enh_Aluminum_6.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_Aluminum_6.tif
    Main color from image: Mask_Enh_Aluminum_6.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((254, 229, 223), 1), ((250, 229, 223), 1), ((248, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_Aluminum_6.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
Aluminum  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 696.532319  Sum Ch 0: 233.779842  Sum Ch 1: 233.779842  Sum Ch 2: 228.972635
          Histog   : 94.37184   N.List elem: 768  Max: 248402 Idx Max: 8   Min: 819 Idx Min: 94
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.563387602119285   Percentage G: 32.873224795761416   Percentage B: 32.873224795761416
File Start: 2019-06-01 19:25:24.494736  Finish: 2019-06-01 19:25:42.008856   File Time: 0:00:17.514120
Brass_1.tif
------------------------------------------------------------------------
Inf.File: Brass_1.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Brass_1.tif
    RGB percent from image: Brass_1.tif
     ------------------------------
     Percent Red 33.63006225345552
     Percent Green 33.63006225345552
     Percent Blue 32.73987549308895
     ------------------------------
Enhancement color: Brass_1.tif   Value: 2.0
   Enhance image: Brass_1.tif   Value: 2.0
   Save enhanced file : Enh_Brass_1.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_Brass_1.tif
   Red background for image: Enh_Brass_1.tif
   Save masked image with red background: Mask_Enh_Brass_1.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_Brass_1.tif
    Main color from image: Mask_Enh_Brass_1.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((251, 229, 223), 1), ((245, 229, 223), 1), ((237, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_Brass_1.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
Brass  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 562.098115  Sum Ch 0: 189.033946  Sum Ch 1: 189.033946  Sum Ch 2: 184.030223
          Histog   : 94.37184   N.List elem: 768  Max: 217593 Idx Max: 252   Min: 249 Idx Min: 0
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.63006225345552   Percentage G: 32.73987549308895   Percentage B: 32.73987549308895
File Start: 2019-06-01 19:25:42.009352  Finish: 2019-06-01 19:25:55.442238   File Time: 0:00:13.432886
Brass_2.tif
------------------------------------------------------------------------
Inf.File: Brass_2.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Brass_2.tif
    RGB percent from image: Brass_2.tif
     ------------------------------
     Percent Red 33.677561630800604
     Percent Green 33.677561630800604
     Percent Blue 32.6448767383988
     ------------------------------
Enhancement color: Brass_2.tif   Value: 2.0
   Enhance image: Brass_2.tif   Value: 2.0
   Save enhanced file : Enh_Brass_2.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_Brass_2.tif
   Red background for image: Enh_Brass_2.tif
   Save masked image with red background: Mask_Enh_Brass_2.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_Brass_2.tif
    Main color from image: Mask_Enh_Brass_2.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((249, 229, 223), 1), ((247, 229, 223), 1), ((245, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_Brass_2.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
Brass  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 449.396523  Sum Ch 0: 151.345791  Sum Ch 1: 151.345791  Sum Ch 2: 146.704941
          Histog   : 94.37184   N.List elem: 768  Max: 179424 Idx Max: 19   Min: 252 Idx Min: 0
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.677561630800604   Percentage G: 32.6448767383988   Percentage B: 32.6448767383988
File Start: 2019-06-01 19:25:55.442737  Finish: 2019-06-01 19:26:08.400309   File Time: 0:00:12.957572
Brass_3.tif
------------------------------------------------------------------------
Inf.File: Brass_3.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Brass_3.tif
    RGB percent from image: Brass_3.tif
     ------------------------------
     Percent Red 33.70125013816265
     Percent Green 33.70125013816265
     Percent Blue 32.597499723674694
     ------------------------------
Enhancement color: Brass_3.tif   Value: 2.0
   Enhance image: Brass_3.tif   Value: 2.0
   Save enhanced file : Enh_Brass_3.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_Brass_3.tif
   Red background for image: Enh_Brass_3.tif
   Save masked image with red background: Mask_Enh_Brass_3.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_Brass_3.tif
    Main color from image: Mask_Enh_Brass_3.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((254, 229, 223), 1), ((248, 229, 223), 1), ((242, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_Brass_3.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
Brass  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 469.763357  Sum Ch 0: 158.316124  Sum Ch 1: 158.316124  Sum Ch 2: 153.131109
          Histog   : 94.37184   N.List elem: 768  Max: 201117 Idx Max: 14   Min: 314 Idx Min: 255
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.70125013816265   Percentage G: 32.597499723674694   Percentage B: 32.597499723674694
File Start: 2019-06-01 19:26:08.400309  Finish: 2019-06-01 19:26:20.148465   File Time: 0:00:11.748156
Brass_4.tif
------------------------------------------------------------------------
Inf.File: Brass_4.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Brass_4.tif
    RGB percent from image: Brass_4.tif
     ------------------------------
     Percent Red 33.63006225345552
     Percent Green 33.63006225345552
     Percent Blue 32.73987549308895
     ------------------------------
Enhancement color: Brass_4.tif   Value: 2.0
   Enhance image: Brass_4.tif   Value: 2.0
   Save enhanced file : Enh_Brass_4.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_Brass_4.tif
   Red background for image: Enh_Brass_4.tif
   Save masked image with red background: Mask_Enh_Brass_4.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_Brass_4.tif
    Main color from image: Mask_Enh_Brass_4.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((251, 229, 223), 1), ((245, 229, 223), 1), ((237, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_Brass_4.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
Brass  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 562.098115  Sum Ch 0: 189.033946  Sum Ch 1: 189.033946  Sum Ch 2: 184.030223
          Histog   : 94.37184   N.List elem: 768  Max: 217593 Idx Max: 252   Min: 249 Idx Min: 0
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.63006225345552   Percentage G: 32.73987549308895   Percentage B: 32.73987549308895
File Start: 2019-06-01 19:26:20.148465  Finish: 2019-06-01 19:26:33.026661   File Time: 0:00:12.878196
Brass_5.tif
------------------------------------------------------------------------
Inf.File: Brass_5.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Brass_5.tif
    RGB percent from image: Brass_5.tif
     ------------------------------
     Percent Red 33.677561630800604
     Percent Green 33.677561630800604
     Percent Blue 32.6448767383988
     ------------------------------
Enhancement color: Brass_5.tif   Value: 2.0
   Enhance image: Brass_5.tif   Value: 2.0
   Save enhanced file : Enh_Brass_5.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_Brass_5.tif
   Red background for image: Enh_Brass_5.tif
   Save masked image with red background: Mask_Enh_Brass_5.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_Brass_5.tif
    Main color from image: Mask_Enh_Brass_5.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((249, 229, 223), 1), ((247, 229, 223), 1), ((245, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_Brass_5.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
Brass  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 449.396523  Sum Ch 0: 151.345791  Sum Ch 1: 151.345791  Sum Ch 2: 146.704941
          Histog   : 94.37184   N.List elem: 768  Max: 179424 Idx Max: 19   Min: 252 Idx Min: 0
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.677561630800604   Percentage G: 32.6448767383988   Percentage B: 32.6448767383988
File Start: 2019-06-01 19:26:33.026661  Finish: 2019-06-01 19:26:45.942219   File Time: 0:00:12.915558
Brass_6.tif
------------------------------------------------------------------------
Inf.File: Brass_6.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Brass_6.tif
    RGB percent from image: Brass_6.tif
     ------------------------------
     Percent Red 33.70125013816265
     Percent Green 33.70125013816265
     Percent Blue 32.597499723674694
     ------------------------------
Enhancement color: Brass_6.tif   Value: 2.0
   Enhance image: Brass_6.tif   Value: 2.0
   Save enhanced file : Enh_Brass_6.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_Brass_6.tif
   Red background for image: Enh_Brass_6.tif
   Save masked image with red background: Mask_Enh_Brass_6.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_Brass_6.tif
    Main color from image: Mask_Enh_Brass_6.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((254, 229, 223), 1), ((248, 229, 223), 1), ((242, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_Brass_6.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
Brass  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 469.763357  Sum Ch 0: 158.316124  Sum Ch 1: 158.316124  Sum Ch 2: 153.131109
          Histog   : 94.37184   N.List elem: 768  Max: 201117 Idx Max: 14   Min: 314 Idx Min: 255
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.70125013816265   Percentage G: 32.597499723674694   Percentage B: 32.597499723674694
File Start: 2019-06-01 19:26:45.942682  Finish: 2019-06-01 19:26:57.818517   File Time: 0:00:11.875835
CopperWire_1.tif
------------------------------------------------------------------------
Inf.File: CopperWire_1.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/CopperWire_1.tif
    RGB percent from image: CopperWire_1.tif
     ------------------------------
     Percent Red 33.65505173811915
     Percent Green 33.65505173811915
     Percent Blue 32.689896523761696
     ------------------------------
Enhancement color: CopperWire_1.tif   Value: 2.0
   Enhance image: CopperWire_1.tif   Value: 2.0
   Save enhanced file : Enh_CopperWire_1.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_CopperWire_1.tif
   Red background for image: Enh_CopperWire_1.tif
   Save masked image with red background: Mask_Enh_CopperWire_1.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_CopperWire_1.tif
    Main color from image: Mask_Enh_CopperWire_1.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((239, 229, 223), 1), ((231, 229, 223), 1), ((229, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_CopperWire_1.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
CopperWire  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 521.051218  Sum Ch 0: 175.360057  Sum Ch 1: 175.360057  Sum Ch 2: 170.331104
          Histog   : 94.37184   N.List elem: 768  Max: 219734 Idx Max: 252   Min: 65 Idx Min: 255
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.65505173811915   Percentage G: 32.689896523761696   Percentage B: 32.689896523761696
File Start: 2019-06-01 19:26:57.819014  Finish: 2019-06-01 19:27:10.114426   File Time: 0:00:12.295412
CopperWire_2.tif
------------------------------------------------------------------------
Inf.File: CopperWire_2.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/CopperWire_2.tif
    RGB percent from image: CopperWire_2.tif
     ------------------------------
     Percent Red 33.60618855121713
     Percent Green 33.60618855121713
     Percent Blue 32.78762289756573
     ------------------------------
Enhancement color: CopperWire_2.tif   Value: 2.0
   Enhance image: CopperWire_2.tif   Value: 2.0
   Save enhanced file : Enh_CopperWire_2.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_CopperWire_2.tif
   Red background for image: Enh_CopperWire_2.tif
   Save masked image with red background: Mask_Enh_CopperWire_2.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_CopperWire_2.tif
    Main color from image: Mask_Enh_CopperWire_2.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((237, 229, 223), 1), ((233, 229, 223), 1), ((245, 228, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_CopperWire_2.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
CopperWire  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 634.394685  Sum Ch 0: 213.195874  Sum Ch 1: 213.195874  Sum Ch 2: 208.002937
          Histog   : 94.37184   N.List elem: 768  Max: 319958 Idx Max: 252   Min: 205 Idx Min: 156
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.60618855121713   Percentage G: 32.78762289756573   Percentage B: 32.78762289756573
File Start: 2019-06-01 19:27:10.114426  Finish: 2019-06-01 19:27:22.893338   File Time: 0:00:12.778912
CopperWire_3.tif
------------------------------------------------------------------------
Inf.File: CopperWire_3.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/CopperWire_3.tif
    RGB percent from image: CopperWire_3.tif
     ------------------------------
     Percent Red 33.671812077620764
     Percent Green 33.671812077620764
     Percent Blue 32.656375844758465
     ------------------------------
Enhancement color: CopperWire_3.tif   Value: 2.0
   Enhance image: CopperWire_3.tif   Value: 2.0
   Save enhanced file : Enh_CopperWire_3.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_CopperWire_3.tif
   Red background for image: Enh_CopperWire_3.tif
   Save masked image with red background: Mask_Enh_CopperWire_3.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_CopperWire_3.tif
    Main color from image: Mask_Enh_CopperWire_3.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((233, 229, 223), 1), ((231, 229, 223), 1), ((229, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_CopperWire_3.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
CopperWire  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 481.236521  Sum Ch 0: 162.041057  Sum Ch 1: 162.041057  Sum Ch 2: 157.154407
          Histog   : 94.37184   N.List elem: 768  Max: 139122 Idx Max: 253   Min: 118 Idx Min: 255
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.671812077620764   Percentage G: 32.656375844758465   Percentage B: 32.656375844758465
File Start: 2019-06-01 19:27:22.893338  Finish: 2019-06-01 19:27:37.218721   File Time: 0:00:14.325383
CopperWire_4.tif
------------------------------------------------------------------------
Inf.File: CopperWire_4.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/CopperWire_4.tif
    RGB percent from image: CopperWire_4.tif
     ------------------------------
     Percent Red 33.6085797831091
     Percent Green 33.6085797831091
     Percent Blue 32.78284043378181
     ------------------------------
Enhancement color: CopperWire_4.tif   Value: 2.0
   Enhance image: CopperWire_4.tif   Value: 2.0
   Save enhanced file : Enh_CopperWire_4.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_CopperWire_4.tif
   Red background for image: Enh_CopperWire_4.tif
   Save masked image with red background: Mask_Enh_CopperWire_4.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_CopperWire_4.tif
    Main color from image: Mask_Enh_CopperWire_4.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((230, 229, 223), 1), ((255, 227, 223), 1), ((250, 227, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_CopperWire_4.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
CopperWire  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 564.848218  Sum Ch 0: 189.837464  Sum Ch 1: 189.837464  Sum Ch 2: 185.17329
          Histog   : 94.37184   N.List elem: 768  Max: 98434 Idx Max: 51   Min: 41 Idx Min: 255
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.6085797831091   Percentage G: 32.78284043378181   Percentage B: 32.78284043378181
File Start: 2019-06-01 19:27:37.218721  Finish: 2019-06-01 19:27:52.338404   File Time: 0:00:15.119683
CopperWire_5.tif
------------------------------------------------------------------------
Inf.File: CopperWire_5.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/CopperWire_5.tif
    RGB percent from image: CopperWire_5.tif
     ------------------------------
     Percent Red 33.65505173811915
     Percent Green 33.65505173811915
     Percent Blue 32.689896523761696
     ------------------------------
Enhancement color: CopperWire_5.tif   Value: 2.0
   Enhance image: CopperWire_5.tif   Value: 2.0
   Save enhanced file : Enh_CopperWire_5.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_CopperWire_5.tif
   Red background for image: Enh_CopperWire_5.tif
   Save masked image with red background: Mask_Enh_CopperWire_5.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_CopperWire_5.tif
    Main color from image: Mask_Enh_CopperWire_5.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((239, 229, 223), 1), ((231, 229, 223), 1), ((229, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_CopperWire_5.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
CopperWire  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 521.051218  Sum Ch 0: 175.360057  Sum Ch 1: 175.360057  Sum Ch 2: 170.331104
          Histog   : 94.37184   N.List elem: 768  Max: 219734 Idx Max: 252   Min: 65 Idx Min: 255
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.65505173811915   Percentage G: 32.689896523761696   Percentage B: 32.689896523761696
File Start: 2019-06-01 19:27:52.338900  Finish: 2019-06-01 19:28:05.232068   File Time: 0:00:12.893168
CopperWire_6.tif
------------------------------------------------------------------------
Inf.File: CopperWire_6.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/CopperWire_6.tif
    RGB percent from image: CopperWire_6.tif
     ------------------------------
     Percent Red 33.60618855121713
     Percent Green 33.60618855121713
     Percent Blue 32.78762289756573
     ------------------------------
Enhancement color: CopperWire_6.tif   Value: 2.0
   Enhance image: CopperWire_6.tif   Value: 2.0
   Save enhanced file : Enh_CopperWire_6.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_CopperWire_6.tif
   Red background for image: Enh_CopperWire_6.tif
   Save masked image with red background: Mask_Enh_CopperWire_6.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_CopperWire_6.tif
    Main color from image: Mask_Enh_CopperWire_6.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((237, 229, 223), 1), ((233, 229, 223), 1), ((245, 228, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_CopperWire_6.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
CopperWire  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 634.394685  Sum Ch 0: 213.195874  Sum Ch 1: 213.195874  Sum Ch 2: 208.002937
          Histog   : 94.37184   N.List elem: 768  Max: 319958 Idx Max: 252   Min: 205 Idx Min: 156
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.60618855121713   Percentage G: 32.78762289756573   Percentage B: 32.78762289756573
File Start: 2019-06-01 19:28:05.232068  Finish: 2019-06-01 19:28:18.144438   File Time: 0:00:12.912370
CopperWire_7.tif
------------------------------------------------------------------------
Inf.File: CopperWire_7.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/CopperWire_7.tif
    RGB percent from image: CopperWire_7.tif
     ------------------------------
     Percent Red 33.671812077620764
     Percent Green 33.671812077620764
     Percent Blue 32.656375844758465
     ------------------------------
Enhancement color: CopperWire_7.tif   Value: 2.0
   Enhance image: CopperWire_7.tif   Value: 2.0
   Save enhanced file : Enh_CopperWire_7.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_CopperWire_7.tif
   Red background for image: Enh_CopperWire_7.tif
   Save masked image with red background: Mask_Enh_CopperWire_7.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_CopperWire_7.tif
    Main color from image: Mask_Enh_CopperWire_7.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((233, 229, 223), 1), ((231, 229, 223), 1), ((229, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_CopperWire_7.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
CopperWire  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 481.236521  Sum Ch 0: 162.041057  Sum Ch 1: 162.041057  Sum Ch 2: 157.154407
          Histog   : 94.37184   N.List elem: 768  Max: 139122 Idx Max: 253   Min: 118 Idx Min: 255
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.671812077620764   Percentage G: 32.656375844758465   Percentage B: 32.656375844758465
File Start: 2019-06-01 19:28:18.144934  Finish: 2019-06-01 19:28:32.117471   File Time: 0:00:13.972537
CopperWire_8.tif
------------------------------------------------------------------------
Inf.File: CopperWire_8.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/CopperWire_8.tif
    RGB percent from image: CopperWire_8.tif
     ------------------------------
     Percent Red 33.6085797831091
     Percent Green 33.6085797831091
     Percent Blue 32.78284043378181
     ------------------------------
Enhancement color: CopperWire_8.tif   Value: 2.0
   Enhance image: CopperWire_8.tif   Value: 2.0
   Save enhanced file : Enh_CopperWire_8.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_CopperWire_8.tif
   Red background for image: Enh_CopperWire_8.tif
   Save masked image with red background: Mask_Enh_CopperWire_8.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_CopperWire_8.tif
    Main color from image: Mask_Enh_CopperWire_8.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((230, 229, 223), 1), ((255, 227, 223), 1), ((250, 227, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_CopperWire_8.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
CopperWire  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 564.848218  Sum Ch 0: 189.837464  Sum Ch 1: 189.837464  Sum Ch 2: 185.17329
          Histog   : 94.37184   N.List elem: 768  Max: 98434 Idx Max: 51   Min: 41 Idx Min: 255
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.6085797831091   Percentage G: 32.78284043378181   Percentage B: 32.78284043378181
File Start: 2019-06-01 19:28:32.117471  Finish: 2019-06-01 19:28:46.480145   File Time: 0:00:14.362674
Copper_1.tif
------------------------------------------------------------------------
Inf.File: Copper_1.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Copper_1.tif
    RGB percent from image: Copper_1.tif
     ------------------------------
     Percent Red 33.68795564293108
     Percent Green 33.68795564293108
     Percent Blue 32.624088714137834
     ------------------------------
Enhancement color: Copper_1.tif   Value: 2.0
   Enhance image: Copper_1.tif   Value: 2.0
   Save enhanced file : Enh_Copper_1.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_Copper_1.tif
   Red background for image: Enh_Copper_1.tif
   Save masked image with red background: Mask_Enh_Copper_1.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_Copper_1.tif
    Main color from image: Mask_Enh_Copper_1.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((249, 229, 223), 1), ((247, 229, 223), 1), ((245, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_Copper_1.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
Copper  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 447.139193  Sum Ch 0: 150.632053  Sum Ch 1: 150.632053  Sum Ch 2: 145.875087
          Histog   : 94.37184   N.List elem: 768  Max: 176926 Idx Max: 15   Min: 289 Idx Min: 255
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.68795564293108   Percentage G: 32.624088714137834   Percentage B: 32.624088714137834
File Start: 2019-06-01 19:28:46.480642  Finish: 2019-06-01 19:28:59.192638   File Time: 0:00:12.711996
Copper_2.tif
------------------------------------------------------------------------
Inf.File: Copper_2.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Copper_2.tif
    RGB percent from image: Copper_2.tif
     ------------------------------
     Percent Red 33.60516338022042
     Percent Green 33.60516338022042
     Percent Blue 32.78967323955916
     ------------------------------
Enhancement color: Copper_2.tif   Value: 2.0
   Enhance image: Copper_2.tif   Value: 2.0
   Save enhanced file : Enh_Copper_2.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_Copper_2.tif
   Red background for image: Enh_Copper_2.tif
   Save masked image with red background: Mask_Enh_Copper_2.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_Copper_2.tif
    Main color from image: Mask_Enh_Copper_2.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((254, 229, 223), 1), ((248, 229, 223), 1), ((244, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_Copper_2.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
Copper  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 588.109624  Sum Ch 0: 197.6352  Sum Ch 1: 197.6352  Sum Ch 2: 192.839224
          Histog   : 94.37184   N.List elem: 768  Max: 179730 Idx Max: 253   Min: 552 Idx Min: 0
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.60516338022042   Percentage G: 32.78967323955916   Percentage B: 32.78967323955916
File Start: 2019-06-01 19:28:59.192638  Finish: 2019-06-01 19:29:13.338557   File Time: 0:00:14.145919
Copper_3.tif
------------------------------------------------------------------------
Inf.File: Copper_3.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Copper_3.tif
    RGB percent from image: Copper_3.tif
     ------------------------------
     Percent Red 33.68795564293108
     Percent Green 33.68795564293108
     Percent Blue 32.624088714137834
     ------------------------------
Enhancement color: Copper_3.tif   Value: 2.0
   Enhance image: Copper_3.tif   Value: 2.0
   Save enhanced file : Enh_Copper_3.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_Copper_3.tif
   Red background for image: Enh_Copper_3.tif
   Save masked image with red background: Mask_Enh_Copper_3.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_Copper_3.tif
    Main color from image: Mask_Enh_Copper_3.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((249, 229, 223), 1), ((247, 229, 223), 1), ((245, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_Copper_3.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
Copper  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 447.139193  Sum Ch 0: 150.632053  Sum Ch 1: 150.632053  Sum Ch 2: 145.875087
          Histog   : 94.37184   N.List elem: 768  Max: 176926 Idx Max: 15   Min: 289 Idx Min: 255
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.68795564293108   Percentage G: 32.624088714137834   Percentage B: 32.624088714137834
File Start: 2019-06-01 19:29:13.338557  Finish: 2019-06-01 19:29:26.485555   File Time: 0:00:13.146998
Copper_4.tif
------------------------------------------------------------------------
Inf.File: Copper_4.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Copper_4.tif
    RGB percent from image: Copper_4.tif
     ------------------------------
     Percent Red 33.60516338022042
     Percent Green 33.60516338022042
     Percent Blue 32.78967323955916
     ------------------------------
Enhancement color: Copper_4.tif   Value: 2.0
   Enhance image: Copper_4.tif   Value: 2.0
   Save enhanced file : Enh_Copper_4.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_Copper_4.tif
   Red background for image: Enh_Copper_4.tif
   Save masked image with red background: Mask_Enh_Copper_4.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_Copper_4.tif
    Main color from image: Mask_Enh_Copper_4.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((254, 229, 223), 1), ((248, 229, 223), 1), ((244, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_Copper_4.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
Copper  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 588.109624  Sum Ch 0: 197.6352  Sum Ch 1: 197.6352  Sum Ch 2: 192.839224
          Histog   : 94.37184   N.List elem: 768  Max: 179730 Idx Max: 253   Min: 552 Idx Min: 0
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.60516338022042   Percentage G: 32.78967323955916   Percentage B: 32.78967323955916
File Start: 2019-06-01 19:29:26.485555  Finish: 2019-06-01 19:29:40.929438   File Time: 0:00:14.443883
Iron_1.tif
------------------------------------------------------------------------
Inf.File: Iron_1.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Iron_1.tif
    RGB percent from image: Iron_1.tif
     ------------------------------
     Percent Red 33.60090837851171
     Percent Green 33.60090837851171
     Percent Blue 32.79818324297658
     ------------------------------
Enhancement color: Iron_1.tif   Value: 2.0
   Enhance image: Iron_1.tif   Value: 2.0
   Save enhanced file : Enh_Iron_1.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_Iron_1.tif
   Red background for image: Enh_Iron_1.tif
   Save masked image with red background: Mask_Enh_Iron_1.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_Iron_1.tif
    Main color from image: Mask_Enh_Iron_1.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((252, 229, 223), 1), ((240, 229, 223), 1), ((234, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_Iron_1.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
Iron  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 641.517597  Sum Ch 0: 215.55574  Sum Ch 1: 215.55574  Sum Ch 2: 210.406117
          Histog   : 94.37184   N.List elem: 768  Max: 295179 Idx Max: 252   Min: 407 Idx Min: 0
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.60090837851171   Percentage G: 32.79818324297658   Percentage B: 32.79818324297658
File Start: 2019-06-01 19:29:40.929933  Finish: 2019-06-01 19:29:57.020671   File Time: 0:00:16.090738
Iron_2.tif
------------------------------------------------------------------------
Inf.File: Iron_2.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Iron_2.tif
    RGB percent from image: Iron_2.tif
     ------------------------------
     Percent Red 33.60090837851171
     Percent Green 33.60090837851171
     Percent Blue 32.79818324297658
     ------------------------------
Enhancement color: Iron_2.tif   Value: 2.0
   Enhance image: Iron_2.tif   Value: 2.0
   Save enhanced file : Enh_Iron_2.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_Iron_2.tif
   Red background for image: Enh_Iron_2.tif
   Save masked image with red background: Mask_Enh_Iron_2.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_Iron_2.tif
    Main color from image: Mask_Enh_Iron_2.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((252, 229, 223), 1), ((240, 229, 223), 1), ((234, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_Iron_2.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
Iron  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 641.517597  Sum Ch 0: 215.55574  Sum Ch 1: 215.55574  Sum Ch 2: 210.406117
          Histog   : 94.37184   N.List elem: 768  Max: 295179 Idx Max: 252   Min: 407 Idx Min: 0
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.60090837851171   Percentage G: 32.79818324297658   Percentage B: 32.79818324297658
File Start: 2019-06-01 19:29:57.021157  Finish: 2019-06-01 19:30:13.379445   File Time: 0:00:16.358288
Iron_3.tif
------------------------------------------------------------------------
Inf.File: Iron_3.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Iron_3.tif
    RGB percent from image: Iron_3.tif
     ------------------------------
     Percent Red 33.629611471923226
     Percent Green 33.629611471923226
     Percent Blue 32.74077705615355
     ------------------------------
Enhancement color: Iron_3.tif   Value: 2.0
   Enhance image: Iron_3.tif   Value: 2.0
   Save enhanced file : Enh_Iron_3.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_Iron_3.tif
   Red background for image: Enh_Iron_3.tif
   Save masked image with red background: Mask_Enh_Iron_3.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_Iron_3.tif
    Main color from image: Mask_Enh_Iron_3.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((254, 229, 223), 1), ((242, 229, 223), 1), ((238, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_Iron_3.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
Iron  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 601.674947  Sum Ch 0: 202.340947  Sum Ch 1: 202.340947  Sum Ch 2: 196.993053
          Histog   : 94.37184   N.List elem: 768  Max: 326204 Idx Max: 252   Min: 303 Idx Min: 255
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.629611471923226   Percentage G: 32.74077705615355   Percentage B: 32.74077705615355
File Start: 2019-06-01 19:30:13.379942  Finish: 2019-06-01 19:30:27.214902   File Time: 0:00:13.834960
Iron_4.tif
------------------------------------------------------------------------
Inf.File: Iron_4.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Iron_4.tif
    RGB percent from image: Iron_4.tif
     ------------------------------
     Percent Red 33.62871679731947
     Percent Green 33.62871679731947
     Percent Blue 32.74256640536107
     ------------------------------
Enhancement color: Iron_4.tif   Value: 2.0
   Enhance image: Iron_4.tif   Value: 2.0
   Save enhanced file : Enh_Iron_4.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_Iron_4.tif
   Red background for image: Enh_Iron_4.tif
   Save masked image with red background: Mask_Enh_Iron_4.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_Iron_4.tif
    Main color from image: Mask_Enh_Iron_4.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((253, 229, 223), 1), ((249, 229, 223), 1), ((247, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_Iron_4.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
Iron  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 575.482113  Sum Ch 0: 193.52725  Sum Ch 1: 193.52725  Sum Ch 2: 188.427613
          Histog   : 94.37184   N.List elem: 768  Max: 297351 Idx Max: 252   Min: 413 Idx Min: 255
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.62871679731947   Percentage G: 32.74256640536107   Percentage B: 32.74256640536107
File Start: 2019-06-01 19:30:27.215396  Finish: 2019-06-01 19:30:42.835676   File Time: 0:00:15.620280
PaintedIron_1.tif
------------------------------------------------------------------------
Inf.File: PaintedIron_1.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/PaintedIron_1.tif
    RGB percent from image: PaintedIron_1.tif
     ------------------------------
     Percent Red 33.66666144410743
     Percent Green 33.66666144410743
     Percent Blue 32.666677111785134
     ------------------------------
Enhancement color: PaintedIron_1.tif   Value: 2.0
   Enhance image: PaintedIron_1.tif   Value: 2.0
   Save enhanced file : Enh_PaintedIron_1.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_PaintedIron_1.tif
   Red background for image: Enh_PaintedIron_1.tif
   Save masked image with red background: Mask_Enh_PaintedIron_1.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_PaintedIron_1.tif
    Main color from image: Mask_Enh_PaintedIron_1.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((226, 225, 223), 1), ((255, 223, 223), 1), ((250, 223, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_PaintedIron_1.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
PaintedIron  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 495.478663  Sum Ch 0: 166.811124  Sum Ch 1: 166.811124  Sum Ch 2: 161.856415
          Histog   : 94.37184   N.List elem: 768  Max: 221927 Idx Max: 253   Min: 65 Idx Min: 176
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.66666144410743   Percentage G: 32.666677111785134   Percentage B: 32.666677111785134
File Start: 2019-06-01 19:30:42.836172  Finish: 2019-06-01 19:30:53.614288   File Time: 0:00:10.778116
PaintedIron_2.tif
------------------------------------------------------------------------
Inf.File: PaintedIron_2.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/PaintedIron_2.tif
    RGB percent from image: PaintedIron_2.tif
     ------------------------------
     Percent Red 33.60762875637241
     Percent Green 33.60762875637241
     Percent Blue 32.78474248725517
     ------------------------------
Enhancement color: PaintedIron_2.tif   Value: 2.0
   Enhance image: PaintedIron_2.tif   Value: 2.0
   Save enhanced file : Enh_PaintedIron_2.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_PaintedIron_2.tif
   Red background for image: Enh_PaintedIron_2.tif
   Save masked image with red background: Mask_Enh_PaintedIron_2.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_PaintedIron_2.tif
    Main color from image: Mask_Enh_PaintedIron_2.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((242, 229, 223), 1), ((234, 229, 223), 1), ((230, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_PaintedIron_2.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
PaintedIron  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 606.481866  Sum Ch 0: 203.824174  Sum Ch 1: 203.824174  Sum Ch 2: 198.833518
          Histog   : 94.37184   N.List elem: 768  Max: 438426 Idx Max: 253   Min: 132 Idx Min: 120
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.60762875637241   Percentage G: 32.78474248725517   Percentage B: 32.78474248725517
File Start: 2019-06-01 19:30:53.614288  Finish: 2019-06-01 19:31:01.149104   File Time: 0:00:07.534816
PaintedIron_3.tif
------------------------------------------------------------------------
Inf.File: PaintedIron_3.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/PaintedIron_3.tif
    RGB percent from image: PaintedIron_3.tif
     ------------------------------
     Percent Red 33.62127297548277
     Percent Green 33.62127297548277
     Percent Blue 32.757454049034465
     ------------------------------
Enhancement color: PaintedIron_3.tif   Value: 2.0
   Enhance image: PaintedIron_3.tif   Value: 2.0
   Save enhanced file : Enh_PaintedIron_3.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_PaintedIron_3.tif
   Red background for image: Enh_PaintedIron_3.tif
   Save masked image with red background: Mask_Enh_PaintedIron_3.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_PaintedIron_3.tif
    Main color from image: Mask_Enh_PaintedIron_3.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((233, 229, 223), 1), ((231, 229, 223), 1), ((229, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_PaintedIron_3.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
PaintedIron  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 573.56847  Sum Ch 0: 192.841021  Sum Ch 1: 192.841021  Sum Ch 2: 187.886428
          Histog   : 94.37184   N.List elem: 768  Max: 374673 Idx Max: 253   Min: 183 Idx Min: 117
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.62127297548277   Percentage G: 32.757454049034465   Percentage B: 32.757454049034465
File Start: 2019-06-01 19:31:01.149104  Finish: 2019-06-01 19:31:08.216060   File Time: 0:00:07.066956
PaintedIron_4.tif
------------------------------------------------------------------------
Inf.File: PaintedIron_4.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/PaintedIron_4.tif
    RGB percent from image: PaintedIron_4.tif
     ------------------------------
     Percent Red 33.66666144410743
     Percent Green 33.66666144410743
     Percent Blue 32.666677111785134
     ------------------------------
Enhancement color: PaintedIron_4.tif   Value: 2.0
   Enhance image: PaintedIron_4.tif   Value: 2.0
   Save enhanced file : Enh_PaintedIron_4.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_PaintedIron_4.tif
   Red background for image: Enh_PaintedIron_4.tif
   Save masked image with red background: Mask_Enh_PaintedIron_4.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_PaintedIron_4.tif
    Main color from image: Mask_Enh_PaintedIron_4.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((226, 225, 223), 1), ((255, 223, 223), 1), ((250, 223, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_PaintedIron_4.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
PaintedIron  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 495.478663  Sum Ch 0: 166.811124  Sum Ch 1: 166.811124  Sum Ch 2: 161.856415
          Histog   : 94.37184   N.List elem: 768  Max: 221927 Idx Max: 253   Min: 65 Idx Min: 176
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.66666144410743   Percentage G: 32.666677111785134   Percentage B: 32.666677111785134
File Start: 2019-06-01 19:31:08.216060  Finish: 2019-06-01 19:31:19.226702   File Time: 0:00:11.010642
PaintedIron_5.tif
------------------------------------------------------------------------
Inf.File: PaintedIron_5.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/PaintedIron_5.tif
    RGB percent from image: PaintedIron_5.tif
     ------------------------------
     Percent Red 33.60762875637241
     Percent Green 33.60762875637241
     Percent Blue 32.78474248725517
     ------------------------------
Enhancement color: PaintedIron_5.tif   Value: 2.0
   Enhance image: PaintedIron_5.tif   Value: 2.0
   Save enhanced file : Enh_PaintedIron_5.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_PaintedIron_5.tif
   Red background for image: Enh_PaintedIron_5.tif
   Save masked image with red background: Mask_Enh_PaintedIron_5.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_PaintedIron_5.tif
    Main color from image: Mask_Enh_PaintedIron_5.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((242, 229, 223), 1), ((234, 229, 223), 1), ((230, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_PaintedIron_5.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
PaintedIron  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 606.481866  Sum Ch 0: 203.824174  Sum Ch 1: 203.824174  Sum Ch 2: 198.833518
          Histog   : 94.37184   N.List elem: 768  Max: 438426 Idx Max: 253   Min: 132 Idx Min: 120
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.60762875637241   Percentage G: 32.78474248725517   Percentage B: 32.78474248725517
File Start: 2019-06-01 19:31:19.227197  Finish: 2019-06-01 19:31:26.666659   File Time: 0:00:07.439462
PaintedIron_6.tif
------------------------------------------------------------------------
Inf.File: PaintedIron_6.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/PaintedIron_6.tif
    RGB percent from image: PaintedIron_6.tif
     ------------------------------
     Percent Red 33.62127297548277
     Percent Green 33.62127297548277
     Percent Blue 32.757454049034465
     ------------------------------
Enhancement color: PaintedIron_6.tif   Value: 2.0
   Enhance image: PaintedIron_6.tif   Value: 2.0
   Save enhanced file : Enh_PaintedIron_6.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_PaintedIron_6.tif
   Red background for image: Enh_PaintedIron_6.tif
   Save masked image with red background: Mask_Enh_PaintedIron_6.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_PaintedIron_6.tif
    Main color from image: Mask_Enh_PaintedIron_6.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((233, 229, 223), 1), ((231, 229, 223), 1), ((229, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_PaintedIron_6.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
PaintedIron  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 573.56847  Sum Ch 0: 192.841021  Sum Ch 1: 192.841021  Sum Ch 2: 187.886428
          Histog   : 94.37184   N.List elem: 768  Max: 374673 Idx Max: 253   Min: 183 Idx Min: 117
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.62127297548277   Percentage G: 32.757454049034465   Percentage B: 32.757454049034465
File Start: 2019-06-01 19:31:26.666659  Finish: 2019-06-01 19:31:33.957321   File Time: 0:00:07.290662
PaintedIron_7.tif
------------------------------------------------------------------------
Inf.File: PaintedIron_7.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/PaintedIron_7.tif
    RGB percent from image: PaintedIron_7.tif
     ------------------------------
     Percent Red 33.60762875637241
     Percent Green 33.60762875637241
     Percent Blue 32.78474248725517
     ------------------------------
Enhancement color: PaintedIron_7.tif   Value: 2.0
   Enhance image: PaintedIron_7.tif   Value: 2.0
   Save enhanced file : Enh_PaintedIron_7.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_PaintedIron_7.tif
   Red background for image: Enh_PaintedIron_7.tif
   Save masked image with red background: Mask_Enh_PaintedIron_7.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_PaintedIron_7.tif
    Main color from image: Mask_Enh_PaintedIron_7.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((242, 229, 223), 1), ((234, 229, 223), 1), ((230, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_PaintedIron_7.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
PaintedIron  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 606.481866  Sum Ch 0: 203.824174  Sum Ch 1: 203.824174  Sum Ch 2: 198.833518
          Histog   : 94.37184   N.List elem: 768  Max: 438426 Idx Max: 253   Min: 132 Idx Min: 120
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.60762875637241   Percentage G: 32.78474248725517   Percentage B: 32.78474248725517
File Start: 2019-06-01 19:31:33.957817  Finish: 2019-06-01 19:31:41.351691   File Time: 0:00:07.393874
StainlessSteel_1.tif
------------------------------------------------------------------------
Inf.File: StainlessSteel_1.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_1.tif
    RGB percent from image: StainlessSteel_1.tif
     ------------------------------
     Percent Red 33.58207473185194
     Percent Green 33.58207473185194
     Percent Blue 32.835850536296114
     ------------------------------
Enhancement color: StainlessSteel_1.tif   Value: 2.0
   Enhance image: StainlessSteel_1.tif   Value: 2.0
   Save enhanced file : Enh_StainlessSteel_1.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_StainlessSteel_1.tif
   Red background for image: Enh_StainlessSteel_1.tif
   Save masked image with red background: Mask_Enh_StainlessSteel_1.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_StainlessSteel_1.tif
    Main color from image: Mask_Enh_StainlessSteel_1.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((255, 229, 223), 1), ((251, 229, 223), 1), ((249, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_StainlessSteel_1.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
StainlessSteel  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 654.906666  Sum Ch 0: 219.931246  Sum Ch 1: 219.931246  Sum Ch 2: 215.044174
          Histog   : 94.37184   N.List elem: 768  Max: 366933 Idx Max: 253   Min: 386 Idx Min: 255
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.58207473185194   Percentage G: 32.835850536296114   Percentage B: 32.835850536296114
File Start: 2019-06-01 19:31:41.352681  Finish: 2019-06-01 19:31:55.033147   File Time: 0:00:13.680466
StainlessSteel_2.tif
------------------------------------------------------------------------
Inf.File: StainlessSteel_2.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_2.tif
    RGB percent from image: StainlessSteel_2.tif
     ------------------------------
     Percent Red 33.61617790672816
     Percent Green 33.61617790672816
     Percent Blue 32.76764418654368
     ------------------------------
Enhancement color: StainlessSteel_2.tif   Value: 2.0
   Enhance image: StainlessSteel_2.tif   Value: 2.0
   Save enhanced file : Enh_StainlessSteel_2.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_StainlessSteel_2.tif
   Red background for image: Enh_StainlessSteel_2.tif
   Save masked image with red background: Mask_Enh_StainlessSteel_2.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_StainlessSteel_2.tif
    Main color from image: Mask_Enh_StainlessSteel_2.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((251, 229, 223), 1), ((245, 229, 223), 1), ((243, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_StainlessSteel_2.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
StainlessSteel  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 573.675257  Sum Ch 0: 192.847695  Sum Ch 1: 192.847695  Sum Ch 2: 187.979867
          Histog   : 94.37184   N.List elem: 768  Max: 356158 Idx Max: 253   Min: 460 Idx Min: 237
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.61617790672816   Percentage G: 32.76764418654368   Percentage B: 32.76764418654368
File Start: 2019-06-01 19:31:55.033642  Finish: 2019-06-01 19:32:09.438525   File Time: 0:00:14.404883
StainlessSteel_3.tif
------------------------------------------------------------------------
Inf.File: StainlessSteel_3.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_3.tif
    RGB percent from image: StainlessSteel_3.tif
     ------------------------------
     Percent Red 33.58207473185194
     Percent Green 33.58207473185194
     Percent Blue 32.835850536296114
     ------------------------------
Enhancement color: StainlessSteel_3.tif   Value: 2.0
   Enhance image: StainlessSteel_3.tif   Value: 2.0
   Save enhanced file : Enh_StainlessSteel_3.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_StainlessSteel_3.tif
   Red background for image: Enh_StainlessSteel_3.tif
   Save masked image with red background: Mask_Enh_StainlessSteel_3.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_StainlessSteel_3.tif
    Main color from image: Mask_Enh_StainlessSteel_3.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((255, 229, 223), 1), ((251, 229, 223), 1), ((249, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_StainlessSteel_3.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
StainlessSteel  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 654.906666  Sum Ch 0: 219.931246  Sum Ch 1: 219.931246  Sum Ch 2: 215.044174
          Histog   : 94.37184   N.List elem: 768  Max: 366933 Idx Max: 253   Min: 386 Idx Min: 255
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.58207473185194   Percentage G: 32.835850536296114   Percentage B: 32.835850536296114
File Start: 2019-06-01 19:32:09.439021  Finish: 2019-06-01 19:32:23.453107   File Time: 0:00:14.014086
StainlessSteel_4.tif
------------------------------------------------------------------------
Inf.File: StainlessSteel_4.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_4.tif
    RGB percent from image: StainlessSteel_4.tif
     ------------------------------
     Percent Red 33.61617790672816
     Percent Green 33.61617790672816
     Percent Blue 32.76764418654368
     ------------------------------
Enhancement color: StainlessSteel_4.tif   Value: 2.0
   Enhance image: StainlessSteel_4.tif   Value: 2.0
   Save enhanced file : Enh_StainlessSteel_4.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_StainlessSteel_4.tif
   Red background for image: Enh_StainlessSteel_4.tif
   Save masked image with red background: Mask_Enh_StainlessSteel_4.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_StainlessSteel_4.tif
    Main color from image: Mask_Enh_StainlessSteel_4.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((251, 229, 223), 1), ((245, 229, 223), 1), ((243, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_StainlessSteel_4.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
StainlessSteel  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 573.675257  Sum Ch 0: 192.847695  Sum Ch 1: 192.847695  Sum Ch 2: 187.979867
          Histog   : 94.37184   N.List elem: 768  Max: 356158 Idx Max: 253   Min: 460 Idx Min: 237
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.61617790672816   Percentage G: 32.76764418654368   Percentage B: 32.76764418654368
File Start: 2019-06-01 19:32:23.453107  Finish: 2019-06-01 19:32:38.191049   File Time: 0:00:14.737942
StainlessSteel_5.tif
------------------------------------------------------------------------
Inf.File: StainlessSteel_5.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_5.tif
    RGB percent from image: StainlessSteel_5.tif
     ------------------------------
     Percent Red 33.60690411510592
     Percent Green 33.60690411510592
     Percent Blue 32.78619176978816
     ------------------------------
Enhancement color: StainlessSteel_5.tif   Value: 2.0
   Enhance image: StainlessSteel_5.tif   Value: 2.0
   Save enhanced file : Enh_StainlessSteel_5.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_StainlessSteel_5.tif
   Red background for image: Enh_StainlessSteel_5.tif
   Save masked image with red background: Mask_Enh_StainlessSteel_5.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_StainlessSteel_5.tif
    Main color from image: Mask_Enh_StainlessSteel_5.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((243, 229, 223), 1), ((235, 229, 223), 1), ((233, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_StainlessSteel_5.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
StainlessSteel  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 638.936045  Sum Ch 0: 214.726624  Sum Ch 1: 214.726624  Sum Ch 2: 209.482797
          Histog   : 94.37184   N.List elem: 768  Max: 370886 Idx Max: 252   Min: 461 Idx Min: 255
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.60690411510592   Percentage G: 32.78619176978816   Percentage B: 32.78619176978816
File Start: 2019-06-01 19:32:38.191546  Finish: 2019-06-01 19:32:53.014882   File Time: 0:00:14.823336
StainlessSteel_6.tif
------------------------------------------------------------------------
Inf.File: StainlessSteel_6.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_6.tif
    RGB percent from image: StainlessSteel_6.tif
     ------------------------------
     Percent Red 33.58479745441777
     Percent Green 33.58479745441777
     Percent Blue 32.83040509116447
     ------------------------------
Enhancement color: StainlessSteel_6.tif   Value: 2.0
   Enhance image: StainlessSteel_6.tif   Value: 2.0
   Save enhanced file : Enh_StainlessSteel_6.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_StainlessSteel_6.tif
   Red background for image: Enh_StainlessSteel_6.tif
   Save masked image with red background: Mask_Enh_StainlessSteel_6.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_StainlessSteel_6.tif
    Main color from image: Mask_Enh_StainlessSteel_6.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((249, 229, 223), 1), ((245, 229, 223), 1), ((241, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_StainlessSteel_6.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
StainlessSteel  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 731.365569  Sum Ch 0: 245.627645  Sum Ch 1: 245.627645  Sum Ch 2: 240.110279
          Histog   : 94.37184   N.List elem: 768  Max: 497534 Idx Max: 252   Min: 236 Idx Min: 255
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.58479745441777   Percentage G: 32.83040509116447   Percentage B: 32.83040509116447
File Start: 2019-06-01 19:32:53.015377  Finish: 2019-06-01 19:33:07.432896   File Time: 0:00:14.417519
StainlessSteel_7.tif
------------------------------------------------------------------------
Inf.File: StainlessSteel_7.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_7.tif
    RGB percent from image: StainlessSteel_7.tif
     ------------------------------
     Percent Red 33.60334018475968
     Percent Green 33.60334018475968
     Percent Blue 32.793319630480624
     ------------------------------
Enhancement color: StainlessSteel_7.tif   Value: 2.0
   Enhance image: StainlessSteel_7.tif   Value: 2.0
   Save enhanced file : Enh_StainlessSteel_7.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_StainlessSteel_7.tif
   Red background for image: Enh_StainlessSteel_7.tif
   Save masked image with red background: Mask_Enh_StainlessSteel_7.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_StainlessSteel_7.tif
    Main color from image: Mask_Enh_StainlessSteel_7.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((249, 229, 223), 1), ((245, 229, 223), 1), ((239, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_StainlessSteel_7.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
StainlessSteel  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 595.649206  Sum Ch 0: 200.158029  Sum Ch 1: 200.158029  Sum Ch 2: 195.333148
          Histog   : 94.37184   N.List elem: 768  Max: 326367 Idx Max: 253   Min: 673 Idx Min: 139
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.60334018475968   Percentage G: 32.793319630480624   Percentage B: 32.793319630480624
File Start: 2019-06-01 19:33:07.432896  Finish: 2019-06-01 19:33:19.170649   File Time: 0:00:11.737753
StainlessSteel_8.tif
------------------------------------------------------------------------
Inf.File: StainlessSteel_8.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_8.tif
    RGB percent from image: StainlessSteel_8.tif
     ------------------------------
     Percent Red 33.59733292716074
     Percent Green 33.59733292716074
     Percent Blue 32.80533414567851
     ------------------------------
Enhancement color: StainlessSteel_8.tif   Value: 2.0
   Enhance image: StainlessSteel_8.tif   Value: 2.0
   Save enhanced file : Enh_StainlessSteel_8.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_StainlessSteel_8.tif
   Red background for image: Enh_StainlessSteel_8.tif
   Save masked image with red background: Mask_Enh_StainlessSteel_8.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_StainlessSteel_8.tif
    Main color from image: Mask_Enh_StainlessSteel_8.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((245, 229, 223), 1), ((243, 229, 223), 1), ((241, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_StainlessSteel_8.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
StainlessSteel  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 611.667734  Sum Ch 0: 205.504045  Sum Ch 1: 205.504045  Sum Ch 2: 200.659644
          Histog   : 94.37184   N.List elem: 768  Max: 357902 Idx Max: 253   Min: 581 Idx Min: 255
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.59733292716074   Percentage G: 32.80533414567851   Percentage B: 32.80533414567851
File Start: 2019-06-01 19:33:19.170649  Finish: 2019-06-01 19:33:33.486505   File Time: 0:00:14.315856
StainlessSteel_9.tif
------------------------------------------------------------------------
Inf.File: StainlessSteel_9.tif
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_9.tif
    RGB percent from image: StainlessSteel_9.tif
     ------------------------------
     Percent Red 33.528715186138044
     Percent Green 33.528715186138044
     Percent Blue 32.94256962772391
     ------------------------------
Enhancement color: StainlessSteel_9.tif   Value: 2.0
   Enhance image: StainlessSteel_9.tif   Value: 2.0
   Save enhanced file : Enh_StainlessSteel_9.tif
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Enh_StainlessSteel_9.tif
   Red background for image: Enh_StainlessSteel_9.tif
   Save masked image with red background: Mask_Enh_StainlessSteel_9.tif
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/ Mask_Enh_StainlessSteel_9.tif
    Main color from image: Mask_Enh_StainlessSteel_9.tif
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 255, 255), 1), ((249, 229, 223), 1), ((243, 229, 223), 1), ((239, 229, 223), 1)]
      Read color name: (255, 255, 255)
      Main Color file: Mask_Enh_StainlessSteel_9.tif  RGB: [((255, 255, 255), 1)] (255, 255, 255)  Color name: white  Hex: #ffffff
StainlessSteel  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 860.058381  Sum Ch 0: 288.366525  Sum Ch 1: 288.366525  Sum Ch 2: 283.325331
          Histog   : 94.37184   N.List elem: 768  Max: 601745 Idx Max: 253   Min: 458 Idx Min: 108
          Color    : white    RGB   : (255, 255, 255)    Hex color: #ffffff   Dec Color: 16777215
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.528715186138044   Percentage G: 32.94256962772391   Percentage B: 32.94256962772391
File Start: 2019-06-01 19:33:33.486505  Finish: 2019-06-01 19:33:48.714942   File Time: 0:00:15.228437
Process Start: 2019-06-01 19:23:48.080869  Finish: 2019-06-01 19:33:48.715437   Global Time: 0:10:00.634568

#list_dec_back ordered
order_list_dec = sorted(list_dec_back, key=int) 
#order_list_dec
#list_non_back 

'''
TESTS
# Read all list to see the color - obtain RGB from int
for x in order_list_dec:
    #print(x)
    # Get RGB from INT
    xrgb = getRGBfromI(x)
    #print('Int:, x,'  RGB: ',xrgb)
    xt_color_name , hexdc = get_rgb_color_name(xrgb)
    print('Int:', x,'  RGB: ', xrgb, xt_color_name)
'''          

"\nTESTS\n# Read all list to see the color - obtain RGB from int\nfor x in order_list_dec:\n    #print(x)\n    # Get RGB from INT\n    xrgb = getRGBfromI(x)\n    #print('Int:, x,'  RGB: ',xrgb)\n    xt_color_name , hexdc = get_rgb_color_name(xrgb)\n    print('Int:', x,'  RGB: ', xrgb, xt_color_name)\n"

df = pd.DataFrame(df_image,columns=['Folder','File','Material','Size','Format','Mode',
                                    'All_Bands', 'Sum_Ch0','Sum_Ch1','Sum_Ch2',
                                    'Histogram','Number_elements',
                                    'Color','Color_RGB', 'Color_hex','Color_dec','Med_Extrems',
                                    'Max_Histog', 'Idx_Max_Histog','Min_Histog', 'Idx_Min_Histog',
                                    'perc_R', 'perc_G', 'perc_B'])
df.head(10)

	Folder	File	Material	Size	Format	Mode	All_Bands	Sum_Ch0	Sum_Ch1	Sum_Ch2	...	Color_hex	Color_dec	Med_Extrems	Max_Histog	Idx_Max_Histog	Min_Histog	Idx_Min_Histog	perc_R	perc_G	perc_B
0	imagedata06	Aluminum_1.tif	Aluminum	(2048, 1536)	TIFF	RGB	658.137536	220.999978	220.999978	216.137580	...	#ffffff	16777215	127.833333	237292	11	543	109	33.579604	33.579604	32.840792
1	imagedata06	Aluminum_2.tif	Aluminum	(2048, 1536)	TIFF	RGB	693.024169	232.705276	232.705276	227.613617	...	#ffffff	16777215	127.833333	280312	252	648	129	33.578234	33.578234	32.843532
2	imagedata06	Aluminum_3.tif	Aluminum	(2048, 1536)	TIFF	RGB	696.532319	233.779842	233.779842	228.972635	...	#ffffff	16777215	127.833333	248402	8	819	94	33.563388	33.563388	32.873225
3	imagedata06	Aluminum_4.tif	Aluminum	(2048, 1536)	TIFF	RGB	658.137536	220.999978	220.999978	216.137580	...	#ffffff	16777215	127.833333	237292	11	543	109	33.579604	33.579604	32.840792
4	imagedata06	Aluminum_5.tif	Aluminum	(2048, 1536)	TIFF	RGB	693.024169	232.705276	232.705276	227.613617	...	#ffffff	16777215	127.833333	280312	252	648	129	33.578234	33.578234	32.843532
5	imagedata06	Aluminum_6.tif	Aluminum	(2048, 1536)	TIFF	RGB	696.532319	233.779842	233.779842	228.972635	...	#ffffff	16777215	127.833333	248402	8	819	94	33.563388	33.563388	32.873225
6	imagedata06	Brass_1.tif	Brass	(2048, 1536)	TIFF	RGB	562.098115	189.033946	189.033946	184.030223	...	#ffffff	16777215	127.833333	217593	252	249	0	33.630062	33.630062	32.739875
7	imagedata06	Brass_2.tif	Brass	(2048, 1536)	TIFF	RGB	449.396523	151.345791	151.345791	146.704941	...	#ffffff	16777215	127.833333	179424	19	252	0	33.677562	33.677562	32.644877
8	imagedata06	Brass_3.tif	Brass	(2048, 1536)	TIFF	RGB	469.763357	158.316124	158.316124	153.131109	...	#ffffff	16777215	127.833333	201117	14	314	255	33.701250	33.701250	32.597500
9	imagedata06	Brass_4.tif	Brass	(2048, 1536)	TIFF	RGB	562.098115	189.033946	189.033946	184.030223	...	#ffffff	16777215	127.833333	217593	252	249	0	33.630062	33.630062	32.739875

10 rows × 24 columns

# Delete junk records
df = df[df.Material != 'MASK']
df = df[df.Material != 'Enh']
df

	Folder	File	Material	Size	Format	Mode	All_Bands	Sum_Ch0	Sum_Ch1	Sum_Ch2	...	Color_hex	Color_dec	Med_Extrems	Max_Histog	Idx_Max_Histog	Min_Histog	Idx_Min_Histog	perc_R	perc_G	perc_B
0	imagedata06	Aluminum_1.tif	Aluminum	(2048, 1536)	TIFF	RGB	658.137536	220.999978	220.999978	216.137580	...	#ffffff	16777215	127.833333	237292	11	543	109	33.579604	33.579604	32.840792
1	imagedata06	Aluminum_2.tif	Aluminum	(2048, 1536)	TIFF	RGB	693.024169	232.705276	232.705276	227.613617	...	#ffffff	16777215	127.833333	280312	252	648	129	33.578234	33.578234	32.843532
2	imagedata06	Aluminum_3.tif	Aluminum	(2048, 1536)	TIFF	RGB	696.532319	233.779842	233.779842	228.972635	...	#ffffff	16777215	127.833333	248402	8	819	94	33.563388	33.563388	32.873225
3	imagedata06	Aluminum_4.tif	Aluminum	(2048, 1536)	TIFF	RGB	658.137536	220.999978	220.999978	216.137580	...	#ffffff	16777215	127.833333	237292	11	543	109	33.579604	33.579604	32.840792
4	imagedata06	Aluminum_5.tif	Aluminum	(2048, 1536)	TIFF	RGB	693.024169	232.705276	232.705276	227.613617	...	#ffffff	16777215	127.833333	280312	252	648	129	33.578234	33.578234	32.843532
5	imagedata06	Aluminum_6.tif	Aluminum	(2048, 1536)	TIFF	RGB	696.532319	233.779842	233.779842	228.972635	...	#ffffff	16777215	127.833333	248402	8	819	94	33.563388	33.563388	32.873225
6	imagedata06	Brass_1.tif	Brass	(2048, 1536)	TIFF	RGB	562.098115	189.033946	189.033946	184.030223	...	#ffffff	16777215	127.833333	217593	252	249	0	33.630062	33.630062	32.739875
7	imagedata06	Brass_2.tif	Brass	(2048, 1536)	TIFF	RGB	449.396523	151.345791	151.345791	146.704941	...	#ffffff	16777215	127.833333	179424	19	252	0	33.677562	33.677562	32.644877
8	imagedata06	Brass_3.tif	Brass	(2048, 1536)	TIFF	RGB	469.763357	158.316124	158.316124	153.131109	...	#ffffff	16777215	127.833333	201117	14	314	255	33.701250	33.701250	32.597500
9	imagedata06	Brass_4.tif	Brass	(2048, 1536)	TIFF	RGB	562.098115	189.033946	189.033946	184.030223	...	#ffffff	16777215	127.833333	217593	252	249	0	33.630062	33.630062	32.739875
10	imagedata06	Brass_5.tif	Brass	(2048, 1536)	TIFF	RGB	449.396523	151.345791	151.345791	146.704941	...	#ffffff	16777215	127.833333	179424	19	252	0	33.677562	33.677562	32.644877
11	imagedata06	Brass_6.tif	Brass	(2048, 1536)	TIFF	RGB	469.763357	158.316124	158.316124	153.131109	...	#ffffff	16777215	127.833333	201117	14	314	255	33.701250	33.701250	32.597500
12	imagedata06	CopperWire_1.tif	CopperWire	(2048, 1536)	TIFF	RGB	521.051218	175.360057	175.360057	170.331104	...	#ffffff	16777215	127.833333	219734	252	65	255	33.655052	33.655052	32.689897
13	imagedata06	CopperWire_2.tif	CopperWire	(2048, 1536)	TIFF	RGB	634.394685	213.195874	213.195874	208.002937	...	#ffffff	16777215	127.833333	319958	252	205	156	33.606189	33.606189	32.787623
14	imagedata06	CopperWire_3.tif	CopperWire	(2048, 1536)	TIFF	RGB	481.236521	162.041057	162.041057	157.154407	...	#ffffff	16777215	127.833333	139122	253	118	255	33.671812	33.671812	32.656376
15	imagedata06	CopperWire_4.tif	CopperWire	(2048, 1536)	TIFF	RGB	564.848218	189.837464	189.837464	185.173290	...	#ffffff	16777215	127.833333	98434	51	41	255	33.608580	33.608580	32.782840
16	imagedata06	CopperWire_5.tif	CopperWire	(2048, 1536)	TIFF	RGB	521.051218	175.360057	175.360057	170.331104	...	#ffffff	16777215	127.833333	219734	252	65	255	33.655052	33.655052	32.689897
17	imagedata06	CopperWire_6.tif	CopperWire	(2048, 1536)	TIFF	RGB	634.394685	213.195874	213.195874	208.002937	...	#ffffff	16777215	127.833333	319958	252	205	156	33.606189	33.606189	32.787623
18	imagedata06	CopperWire_7.tif	CopperWire	(2048, 1536)	TIFF	RGB	481.236521	162.041057	162.041057	157.154407	...	#ffffff	16777215	127.833333	139122	253	118	255	33.671812	33.671812	32.656376
19	imagedata06	CopperWire_8.tif	CopperWire	(2048, 1536)	TIFF	RGB	564.848218	189.837464	189.837464	185.173290	...	#ffffff	16777215	127.833333	98434	51	41	255	33.608580	33.608580	32.782840
20	imagedata06	Copper_1.tif	Copper	(2048, 1536)	TIFF	RGB	447.139193	150.632053	150.632053	145.875087	...	#ffffff	16777215	127.833333	176926	15	289	255	33.687956	33.687956	32.624089
21	imagedata06	Copper_2.tif	Copper	(2048, 1536)	TIFF	RGB	588.109624	197.635200	197.635200	192.839224	...	#ffffff	16777215	127.833333	179730	253	552	0	33.605163	33.605163	32.789673
22	imagedata06	Copper_3.tif	Copper	(2048, 1536)	TIFF	RGB	447.139193	150.632053	150.632053	145.875087	...	#ffffff	16777215	127.833333	176926	15	289	255	33.687956	33.687956	32.624089
23	imagedata06	Copper_4.tif	Copper	(2048, 1536)	TIFF	RGB	588.109624	197.635200	197.635200	192.839224	...	#ffffff	16777215	127.833333	179730	253	552	0	33.605163	33.605163	32.789673
24	imagedata06	Iron_1.tif	Iron	(2048, 1536)	TIFF	RGB	641.517597	215.555740	215.555740	210.406117	...	#ffffff	16777215	127.833333	295179	252	407	0	33.600908	33.600908	32.798183
25	imagedata06	Iron_2.tif	Iron	(2048, 1536)	TIFF	RGB	641.517597	215.555740	215.555740	210.406117	...	#ffffff	16777215	127.833333	295179	252	407	0	33.600908	33.600908	32.798183
26	imagedata06	Iron_3.tif	Iron	(2048, 1536)	TIFF	RGB	601.674947	202.340947	202.340947	196.993053	...	#ffffff	16777215	127.833333	326204	252	303	255	33.629611	33.629611	32.740777
27	imagedata06	Iron_4.tif	Iron	(2048, 1536)	TIFF	RGB	575.482113	193.527250	193.527250	188.427613	...	#ffffff	16777215	127.833333	297351	252	413	255	33.628717	33.628717	32.742566
28	imagedata06	PaintedIron_1.tif	PaintedIron	(2048, 1536)	TIFF	RGB	495.478663	166.811124	166.811124	161.856415	...	#ffffff	16777215	127.833333	221927	253	65	176	33.666661	33.666661	32.666677
29	imagedata06	PaintedIron_2.tif	PaintedIron	(2048, 1536)	TIFF	RGB	606.481866	203.824174	203.824174	198.833518	...	#ffffff	16777215	127.833333	438426	253	132	120	33.607629	33.607629	32.784742
30	imagedata06	PaintedIron_3.tif	PaintedIron	(2048, 1536)	TIFF	RGB	573.568470	192.841021	192.841021	187.886428	...	#ffffff	16777215	127.833333	374673	253	183	117	33.621273	33.621273	32.757454
31	imagedata06	PaintedIron_4.tif	PaintedIron	(2048, 1536)	TIFF	RGB	495.478663	166.811124	166.811124	161.856415	...	#ffffff	16777215	127.833333	221927	253	65	176	33.666661	33.666661	32.666677
32	imagedata06	PaintedIron_5.tif	PaintedIron	(2048, 1536)	TIFF	RGB	606.481866	203.824174	203.824174	198.833518	...	#ffffff	16777215	127.833333	438426	253	132	120	33.607629	33.607629	32.784742
33	imagedata06	PaintedIron_6.tif	PaintedIron	(2048, 1536)	TIFF	RGB	573.568470	192.841021	192.841021	187.886428	...	#ffffff	16777215	127.833333	374673	253	183	117	33.621273	33.621273	32.757454
34	imagedata06	PaintedIron_7.tif	PaintedIron	(2048, 1536)	TIFF	RGB	606.481866	203.824174	203.824174	198.833518	...	#ffffff	16777215	127.833333	438426	253	132	120	33.607629	33.607629	32.784742
35	imagedata06	StainlessSteel_1.tif	StainlessSteel	(2048, 1536)	TIFF	RGB	654.906666	219.931246	219.931246	215.044174	...	#ffffff	16777215	127.833333	366933	253	386	255	33.582075	33.582075	32.835851
36	imagedata06	StainlessSteel_2.tif	StainlessSteel	(2048, 1536)	TIFF	RGB	573.675257	192.847695	192.847695	187.979867	...	#ffffff	16777215	127.833333	356158	253	460	237	33.616178	33.616178	32.767644
37	imagedata06	StainlessSteel_3.tif	StainlessSteel	(2048, 1536)	TIFF	RGB	654.906666	219.931246	219.931246	215.044174	...	#ffffff	16777215	127.833333	366933	253	386	255	33.582075	33.582075	32.835851
38	imagedata06	StainlessSteel_4.tif	StainlessSteel	(2048, 1536)	TIFF	RGB	573.675257	192.847695	192.847695	187.979867	...	#ffffff	16777215	127.833333	356158	253	460	237	33.616178	33.616178	32.767644
39	imagedata06	StainlessSteel_5.tif	StainlessSteel	(2048, 1536)	TIFF	RGB	638.936045	214.726624	214.726624	209.482797	...	#ffffff	16777215	127.833333	370886	252	461	255	33.606904	33.606904	32.786192
40	imagedata06	StainlessSteel_6.tif	StainlessSteel	(2048, 1536)	TIFF	RGB	731.365569	245.627645	245.627645	240.110279	...	#ffffff	16777215	127.833333	497534	252	236	255	33.584797	33.584797	32.830405
41	imagedata06	StainlessSteel_7.tif	StainlessSteel	(2048, 1536)	TIFF	RGB	595.649206	200.158029	200.158029	195.333148	...	#ffffff	16777215	127.833333	326367	253	673	139	33.603340	33.603340	32.793320
42	imagedata06	StainlessSteel_8.tif	StainlessSteel	(2048, 1536)	TIFF	RGB	611.667734	205.504045	205.504045	200.659644	...	#ffffff	16777215	127.833333	357902	253	581	255	33.597333	33.597333	32.805334
43	imagedata06	StainlessSteel_9.tif	StainlessSteel	(2048, 1536)	TIFF	RGB	860.058381	288.366525	288.366525	283.325331	...	#ffffff	16777215	127.833333	601745	253	458	108	33.528715	33.528715	32.942570

44 rows × 24 columns

Write statistics in excel book

# Verify my current folder
path = mypath + r"/upt_data.xlsx"
print('Write statistics into file :', path)

# Block to Read excel old excel file
book = load_workbook(path)
writer = pd.ExcelWriter(path, engine = 'openpyxl')
writer.book = book
# ------------------------

# Write statistics into excel file
#writer = pd.ExcelWriter(path, engine = 'xlsxwriter') # only for new excelfile
df.to_excel(writer, sheet_name = folder)
writer.save()
writer.close()

Write statistics into file : C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/upt_data.xlsx

Plot

df_plot = pd.DataFrame(df, columns=["Material", "All_Bands", "Sum_Ch0", "Sum_Ch1", "Sum_Ch2",
                                    "Color", "Color_RGB", "Color_hex","Color_dec",
                                    "Med_Extrems", "Max_Histog", "Idx_Max_Histog","Min_Histog",
                                    "Idx_Min_Histog","perc_R", "perc_G", "perc_B"])
df_plot

	Material	All_Bands	Sum_Ch0	Sum_Ch1	Sum_Ch2	Color	Color_RGB	Color_hex	Color_dec	Med_Extrems	Max_Histog	Idx_Max_Histog	Min_Histog	Idx_Min_Histog	perc_R	perc_G	perc_B
0	Aluminum	658.137536	220.999978	220.999978	216.137580	white	(255, 255, 255)	#ffffff	16777215	127.833333	237292	11	543	109	33.579604	33.579604	32.840792
1	Aluminum	693.024169	232.705276	232.705276	227.613617	white	(255, 255, 255)	#ffffff	16777215	127.833333	280312	252	648	129	33.578234	33.578234	32.843532
2	Aluminum	696.532319	233.779842	233.779842	228.972635	white	(255, 255, 255)	#ffffff	16777215	127.833333	248402	8	819	94	33.563388	33.563388	32.873225
3	Aluminum	658.137536	220.999978	220.999978	216.137580	white	(255, 255, 255)	#ffffff	16777215	127.833333	237292	11	543	109	33.579604	33.579604	32.840792
4	Aluminum	693.024169	232.705276	232.705276	227.613617	white	(255, 255, 255)	#ffffff	16777215	127.833333	280312	252	648	129	33.578234	33.578234	32.843532
5	Aluminum	696.532319	233.779842	233.779842	228.972635	white	(255, 255, 255)	#ffffff	16777215	127.833333	248402	8	819	94	33.563388	33.563388	32.873225
6	Brass	562.098115	189.033946	189.033946	184.030223	white	(255, 255, 255)	#ffffff	16777215	127.833333	217593	252	249	0	33.630062	33.630062	32.739875
7	Brass	449.396523	151.345791	151.345791	146.704941	white	(255, 255, 255)	#ffffff	16777215	127.833333	179424	19	252	0	33.677562	33.677562	32.644877
8	Brass	469.763357	158.316124	158.316124	153.131109	white	(255, 255, 255)	#ffffff	16777215	127.833333	201117	14	314	255	33.701250	33.701250	32.597500
9	Brass	562.098115	189.033946	189.033946	184.030223	white	(255, 255, 255)	#ffffff	16777215	127.833333	217593	252	249	0	33.630062	33.630062	32.739875
10	Brass	449.396523	151.345791	151.345791	146.704941	white	(255, 255, 255)	#ffffff	16777215	127.833333	179424	19	252	0	33.677562	33.677562	32.644877
11	Brass	469.763357	158.316124	158.316124	153.131109	white	(255, 255, 255)	#ffffff	16777215	127.833333	201117	14	314	255	33.701250	33.701250	32.597500
12	CopperWire	521.051218	175.360057	175.360057	170.331104	white	(255, 255, 255)	#ffffff	16777215	127.833333	219734	252	65	255	33.655052	33.655052	32.689897
13	CopperWire	634.394685	213.195874	213.195874	208.002937	white	(255, 255, 255)	#ffffff	16777215	127.833333	319958	252	205	156	33.606189	33.606189	32.787623
14	CopperWire	481.236521	162.041057	162.041057	157.154407	white	(255, 255, 255)	#ffffff	16777215	127.833333	139122	253	118	255	33.671812	33.671812	32.656376
15	CopperWire	564.848218	189.837464	189.837464	185.173290	white	(255, 255, 255)	#ffffff	16777215	127.833333	98434	51	41	255	33.608580	33.608580	32.782840
16	CopperWire	521.051218	175.360057	175.360057	170.331104	white	(255, 255, 255)	#ffffff	16777215	127.833333	219734	252	65	255	33.655052	33.655052	32.689897
17	CopperWire	634.394685	213.195874	213.195874	208.002937	white	(255, 255, 255)	#ffffff	16777215	127.833333	319958	252	205	156	33.606189	33.606189	32.787623
18	CopperWire	481.236521	162.041057	162.041057	157.154407	white	(255, 255, 255)	#ffffff	16777215	127.833333	139122	253	118	255	33.671812	33.671812	32.656376
19	CopperWire	564.848218	189.837464	189.837464	185.173290	white	(255, 255, 255)	#ffffff	16777215	127.833333	98434	51	41	255	33.608580	33.608580	32.782840
20	Copper	447.139193	150.632053	150.632053	145.875087	white	(255, 255, 255)	#ffffff	16777215	127.833333	176926	15	289	255	33.687956	33.687956	32.624089
21	Copper	588.109624	197.635200	197.635200	192.839224	white	(255, 255, 255)	#ffffff	16777215	127.833333	179730	253	552	0	33.605163	33.605163	32.789673
22	Copper	447.139193	150.632053	150.632053	145.875087	white	(255, 255, 255)	#ffffff	16777215	127.833333	176926	15	289	255	33.687956	33.687956	32.624089
23	Copper	588.109624	197.635200	197.635200	192.839224	white	(255, 255, 255)	#ffffff	16777215	127.833333	179730	253	552	0	33.605163	33.605163	32.789673
24	Iron	641.517597	215.555740	215.555740	210.406117	white	(255, 255, 255)	#ffffff	16777215	127.833333	295179	252	407	0	33.600908	33.600908	32.798183
25	Iron	641.517597	215.555740	215.555740	210.406117	white	(255, 255, 255)	#ffffff	16777215	127.833333	295179	252	407	0	33.600908	33.600908	32.798183
26	Iron	601.674947	202.340947	202.340947	196.993053	white	(255, 255, 255)	#ffffff	16777215	127.833333	326204	252	303	255	33.629611	33.629611	32.740777
27	Iron	575.482113	193.527250	193.527250	188.427613	white	(255, 255, 255)	#ffffff	16777215	127.833333	297351	252	413	255	33.628717	33.628717	32.742566
28	PaintedIron	495.478663	166.811124	166.811124	161.856415	white	(255, 255, 255)	#ffffff	16777215	127.833333	221927	253	65	176	33.666661	33.666661	32.666677
29	PaintedIron	606.481866	203.824174	203.824174	198.833518	white	(255, 255, 255)	#ffffff	16777215	127.833333	438426	253	132	120	33.607629	33.607629	32.784742
30	PaintedIron	573.568470	192.841021	192.841021	187.886428	white	(255, 255, 255)	#ffffff	16777215	127.833333	374673	253	183	117	33.621273	33.621273	32.757454
31	PaintedIron	495.478663	166.811124	166.811124	161.856415	white	(255, 255, 255)	#ffffff	16777215	127.833333	221927	253	65	176	33.666661	33.666661	32.666677
32	PaintedIron	606.481866	203.824174	203.824174	198.833518	white	(255, 255, 255)	#ffffff	16777215	127.833333	438426	253	132	120	33.607629	33.607629	32.784742
33	PaintedIron	573.568470	192.841021	192.841021	187.886428	white	(255, 255, 255)	#ffffff	16777215	127.833333	374673	253	183	117	33.621273	33.621273	32.757454
34	PaintedIron	606.481866	203.824174	203.824174	198.833518	white	(255, 255, 255)	#ffffff	16777215	127.833333	438426	253	132	120	33.607629	33.607629	32.784742
35	StainlessSteel	654.906666	219.931246	219.931246	215.044174	white	(255, 255, 255)	#ffffff	16777215	127.833333	366933	253	386	255	33.582075	33.582075	32.835851
36	StainlessSteel	573.675257	192.847695	192.847695	187.979867	white	(255, 255, 255)	#ffffff	16777215	127.833333	356158	253	460	237	33.616178	33.616178	32.767644
37	StainlessSteel	654.906666	219.931246	219.931246	215.044174	white	(255, 255, 255)	#ffffff	16777215	127.833333	366933	253	386	255	33.582075	33.582075	32.835851
38	StainlessSteel	573.675257	192.847695	192.847695	187.979867	white	(255, 255, 255)	#ffffff	16777215	127.833333	356158	253	460	237	33.616178	33.616178	32.767644
39	StainlessSteel	638.936045	214.726624	214.726624	209.482797	white	(255, 255, 255)	#ffffff	16777215	127.833333	370886	252	461	255	33.606904	33.606904	32.786192
40	StainlessSteel	731.365569	245.627645	245.627645	240.110279	white	(255, 255, 255)	#ffffff	16777215	127.833333	497534	252	236	255	33.584797	33.584797	32.830405
41	StainlessSteel	595.649206	200.158029	200.158029	195.333148	white	(255, 255, 255)	#ffffff	16777215	127.833333	326367	253	673	139	33.603340	33.603340	32.793320
42	StainlessSteel	611.667734	205.504045	205.504045	200.659644	white	(255, 255, 255)	#ffffff	16777215	127.833333	357902	253	581	255	33.597333	33.597333	32.805334
43	StainlessSteel	860.058381	288.366525	288.366525	283.325331	white	(255, 255, 255)	#ffffff	16777215	127.833333	601745	253	458	108	33.528715	33.528715	32.942570

# Adjust values to plot
df_plot.Sum_Ch0        = df_plot.Sum_Ch0 + 500 # to have diference lines during plot
df_plot.Sum_Ch1        = df_plot.Sum_Ch1 + 1000
df_plot.Sum_Ch2        = df_plot.Sum_Ch2 + 1500
df_plot.All_Bands      = df_plot.All_Bands + 2000

df_plot.Color_dec      = df_plot.Color_dec / 1000
df_plot.Color_dec      = df_plot.Color_dec - 10000
df_plot.Med_Extrems    = df_plot.Med_Extrems + 500
df_plot.Max_Histog     = df_plot.Max_Histog / 1000
df_plot.Idx_Max_Histog = df_plot.Idx_Max_Histog + 1000
df_plot.Min_Histog     = df_plot.Min_Histog * 100
df_plot.Idx_Min_Histog = df_plot.Idx_Min_Histog * 10

df_plot.perc_R = df_plot.perc_R + 1000
df_plot.perc_G = df_plot.perc_G + 1100
df_plot.perc_B = df_plot.perc_B + 1200

df_plot

	Material	All_Bands	Sum_Ch0	Sum_Ch1	Sum_Ch2	Color	Color_RGB	Color_hex	Color_dec	Med_Extrems	Max_Histog	Idx_Max_Histog	Min_Histog	Idx_Min_Histog	perc_R	perc_G	perc_B
0	Aluminum	2658.137536	720.999978	1220.999978	1716.137580	white	(255, 255, 255)	#ffffff	6777.215	627.833333	237.292	1011	54300	1090	1033.579604	1133.579604	1232.840792
1	Aluminum	2693.024169	732.705276	1232.705276	1727.613617	white	(255, 255, 255)	#ffffff	6777.215	627.833333	280.312	1252	64800	1290	1033.578234	1133.578234	1232.843532
2	Aluminum	2696.532319	733.779842	1233.779842	1728.972635	white	(255, 255, 255)	#ffffff	6777.215	627.833333	248.402	1008	81900	940	1033.563388	1133.563388	1232.873225
3	Aluminum	2658.137536	720.999978	1220.999978	1716.137580	white	(255, 255, 255)	#ffffff	6777.215	627.833333	237.292	1011	54300	1090	1033.579604	1133.579604	1232.840792
4	Aluminum	2693.024169	732.705276	1232.705276	1727.613617	white	(255, 255, 255)	#ffffff	6777.215	627.833333	280.312	1252	64800	1290	1033.578234	1133.578234	1232.843532
5	Aluminum	2696.532319	733.779842	1233.779842	1728.972635	white	(255, 255, 255)	#ffffff	6777.215	627.833333	248.402	1008	81900	940	1033.563388	1133.563388	1232.873225
6	Brass	2562.098115	689.033946	1189.033946	1684.030223	white	(255, 255, 255)	#ffffff	6777.215	627.833333	217.593	1252	24900	0	1033.630062	1133.630062	1232.739875
7	Brass	2449.396523	651.345791	1151.345791	1646.704941	white	(255, 255, 255)	#ffffff	6777.215	627.833333	179.424	1019	25200	0	1033.677562	1133.677562	1232.644877
8	Brass	2469.763357	658.316124	1158.316124	1653.131109	white	(255, 255, 255)	#ffffff	6777.215	627.833333	201.117	1014	31400	2550	1033.701250	1133.701250	1232.597500
9	Brass	2562.098115	689.033946	1189.033946	1684.030223	white	(255, 255, 255)	#ffffff	6777.215	627.833333	217.593	1252	24900	0	1033.630062	1133.630062	1232.739875
10	Brass	2449.396523	651.345791	1151.345791	1646.704941	white	(255, 255, 255)	#ffffff	6777.215	627.833333	179.424	1019	25200	0	1033.677562	1133.677562	1232.644877
11	Brass	2469.763357	658.316124	1158.316124	1653.131109	white	(255, 255, 255)	#ffffff	6777.215	627.833333	201.117	1014	31400	2550	1033.701250	1133.701250	1232.597500
12	CopperWire	2521.051218	675.360057	1175.360057	1670.331104	white	(255, 255, 255)	#ffffff	6777.215	627.833333	219.734	1252	6500	2550	1033.655052	1133.655052	1232.689897
13	CopperWire	2634.394685	713.195874	1213.195874	1708.002937	white	(255, 255, 255)	#ffffff	6777.215	627.833333	319.958	1252	20500	1560	1033.606189	1133.606189	1232.787623
14	CopperWire	2481.236521	662.041057	1162.041057	1657.154407	white	(255, 255, 255)	#ffffff	6777.215	627.833333	139.122	1253	11800	2550	1033.671812	1133.671812	1232.656376
15	CopperWire	2564.848218	689.837464	1189.837464	1685.173290	white	(255, 255, 255)	#ffffff	6777.215	627.833333	98.434	1051	4100	2550	1033.608580	1133.608580	1232.782840
16	CopperWire	2521.051218	675.360057	1175.360057	1670.331104	white	(255, 255, 255)	#ffffff	6777.215	627.833333	219.734	1252	6500	2550	1033.655052	1133.655052	1232.689897
17	CopperWire	2634.394685	713.195874	1213.195874	1708.002937	white	(255, 255, 255)	#ffffff	6777.215	627.833333	319.958	1252	20500	1560	1033.606189	1133.606189	1232.787623
18	CopperWire	2481.236521	662.041057	1162.041057	1657.154407	white	(255, 255, 255)	#ffffff	6777.215	627.833333	139.122	1253	11800	2550	1033.671812	1133.671812	1232.656376
19	CopperWire	2564.848218	689.837464	1189.837464	1685.173290	white	(255, 255, 255)	#ffffff	6777.215	627.833333	98.434	1051	4100	2550	1033.608580	1133.608580	1232.782840
20	Copper	2447.139193	650.632053	1150.632053	1645.875087	white	(255, 255, 255)	#ffffff	6777.215	627.833333	176.926	1015	28900	2550	1033.687956	1133.687956	1232.624089
21	Copper	2588.109624	697.635200	1197.635200	1692.839224	white	(255, 255, 255)	#ffffff	6777.215	627.833333	179.730	1253	55200	0	1033.605163	1133.605163	1232.789673
22	Copper	2447.139193	650.632053	1150.632053	1645.875087	white	(255, 255, 255)	#ffffff	6777.215	627.833333	176.926	1015	28900	2550	1033.687956	1133.687956	1232.624089
23	Copper	2588.109624	697.635200	1197.635200	1692.839224	white	(255, 255, 255)	#ffffff	6777.215	627.833333	179.730	1253	55200	0	1033.605163	1133.605163	1232.789673
24	Iron	2641.517597	715.555740	1215.555740	1710.406117	white	(255, 255, 255)	#ffffff	6777.215	627.833333	295.179	1252	40700	0	1033.600908	1133.600908	1232.798183
25	Iron	2641.517597	715.555740	1215.555740	1710.406117	white	(255, 255, 255)	#ffffff	6777.215	627.833333	295.179	1252	40700	0	1033.600908	1133.600908	1232.798183
26	Iron	2601.674947	702.340947	1202.340947	1696.993053	white	(255, 255, 255)	#ffffff	6777.215	627.833333	326.204	1252	30300	2550	1033.629611	1133.629611	1232.740777
27	Iron	2575.482113	693.527250	1193.527250	1688.427613	white	(255, 255, 255)	#ffffff	6777.215	627.833333	297.351	1252	41300	2550	1033.628717	1133.628717	1232.742566
28	PaintedIron	2495.478663	666.811124	1166.811124	1661.856415	white	(255, 255, 255)	#ffffff	6777.215	627.833333	221.927	1253	6500	1760	1033.666661	1133.666661	1232.666677
29	PaintedIron	2606.481866	703.824174	1203.824174	1698.833518	white	(255, 255, 255)	#ffffff	6777.215	627.833333	438.426	1253	13200	1200	1033.607629	1133.607629	1232.784742
30	PaintedIron	2573.568470	692.841021	1192.841021	1687.886428	white	(255, 255, 255)	#ffffff	6777.215	627.833333	374.673	1253	18300	1170	1033.621273	1133.621273	1232.757454
31	PaintedIron	2495.478663	666.811124	1166.811124	1661.856415	white	(255, 255, 255)	#ffffff	6777.215	627.833333	221.927	1253	6500	1760	1033.666661	1133.666661	1232.666677
32	PaintedIron	2606.481866	703.824174	1203.824174	1698.833518	white	(255, 255, 255)	#ffffff	6777.215	627.833333	438.426	1253	13200	1200	1033.607629	1133.607629	1232.784742
33	PaintedIron	2573.568470	692.841021	1192.841021	1687.886428	white	(255, 255, 255)	#ffffff	6777.215	627.833333	374.673	1253	18300	1170	1033.621273	1133.621273	1232.757454
34	PaintedIron	2606.481866	703.824174	1203.824174	1698.833518	white	(255, 255, 255)	#ffffff	6777.215	627.833333	438.426	1253	13200	1200	1033.607629	1133.607629	1232.784742
35	StainlessSteel	2654.906666	719.931246	1219.931246	1715.044174	white	(255, 255, 255)	#ffffff	6777.215	627.833333	366.933	1253	38600	2550	1033.582075	1133.582075	1232.835851
36	StainlessSteel	2573.675257	692.847695	1192.847695	1687.979867	white	(255, 255, 255)	#ffffff	6777.215	627.833333	356.158	1253	46000	2370	1033.616178	1133.616178	1232.767644
37	StainlessSteel	2654.906666	719.931246	1219.931246	1715.044174	white	(255, 255, 255)	#ffffff	6777.215	627.833333	366.933	1253	38600	2550	1033.582075	1133.582075	1232.835851
38	StainlessSteel	2573.675257	692.847695	1192.847695	1687.979867	white	(255, 255, 255)	#ffffff	6777.215	627.833333	356.158	1253	46000	2370	1033.616178	1133.616178	1232.767644
39	StainlessSteel	2638.936045	714.726624	1214.726624	1709.482797	white	(255, 255, 255)	#ffffff	6777.215	627.833333	370.886	1252	46100	2550	1033.606904	1133.606904	1232.786192
40	StainlessSteel	2731.365569	745.627645	1245.627645	1740.110279	white	(255, 255, 255)	#ffffff	6777.215	627.833333	497.534	1252	23600	2550	1033.584797	1133.584797	1232.830405
41	StainlessSteel	2595.649206	700.158029	1200.158029	1695.333148	white	(255, 255, 255)	#ffffff	6777.215	627.833333	326.367	1253	67300	1390	1033.603340	1133.603340	1232.793320
42	StainlessSteel	2611.667734	705.504045	1205.504045	1700.659644	white	(255, 255, 255)	#ffffff	6777.215	627.833333	357.902	1253	58100	2550	1033.597333	1133.597333	1232.805334
43	StainlessSteel	2860.058381	788.366525	1288.366525	1783.325331	white	(255, 255, 255)	#ffffff	6777.215	627.833333	601.745	1253	45800	1080	1033.528715	1133.528715	1232.942570

df_plot.plot(y=["All_Bands","Sum_Ch0","Sum_Ch1", "Sum_Ch2","Color_dec","Med_Extrems"],
             figsize=(12,6), grid=True )

# Obtain legend (xticks) for X axis
loc_Array_sum = np.arange(len(df_plot.index))
# Position of X labels
xtick_loc = list(loc_Array_sum)  
# Name of x labels
xticks = list(df_plot.Material)
#-------

#plt.plot(df_plot.Array_sum)
plt.title('IMAGE WITH ALL CHANNELS',fontsize=20)
plt.ylabel('Sum of image matrix',fontsize=18)
plt.xticks(xtick_loc, df_plot.Material, rotation=90)
plt.xlabel('Material',fontsize=18)
plt.legend(loc='upper right', ncol=3, fancybox=True, shadow=True)
plt.savefig(folder+"_Line Graph all channels information.png")
plt.show()

df_plot.perc_R = df_plot.perc_R - 1000
df_plot.perc_G = df_plot.perc_G - 1100
df_plot.perc_B = df_plot.perc_B - 1200

df_plot.plot(y=["perc_R","perc_G","perc_B"],
             figsize=(12,6), grid=True )

# Obtain legend (xticks) for X axis
loc_Array_sum = np.arange(len(df_plot.index))
# Position of X labels
xtick_loc = list(loc_Array_sum)  
# Name of x labels
xticks = list(df_plot.Material)
#-------

#plt.plot(df_plot.Array_sum)
plt.title('Percentage R,G,B',fontsize=20)
plt.ylabel('Percentages R,G,B',fontsize=18)
plt.xticks(xtick_loc, df_plot.Material, rotation=90)
plt.xlabel('Material',fontsize=18)
plt.legend(loc='upper right', ncol=3, fancybox=True, shadow=True)
plt.savefig(folder+"_Line Graph Percentage RGB.png")
plt.show()

# Create pivot table
df_plot1 = df_plot.groupby('Material')['All_Bands', 'Sum_Ch0','Sum_Ch1','Sum_Ch2','Color_dec',
                                       'Med_Extrems', 'Max_Histog', 'Idx_Max_Histog','Min_Histog',
                                       'Idx_Min_Histog','perc_R','perc_G','perc_B'].mean()
df_plot1

	All_Bands	Sum_Ch0	Sum_Ch1	Sum_Ch2	Color_dec	Med_Extrems	Max_Histog	Idx_Max_Histog	Min_Histog	Idx_Min_Histog	perc_R	perc_G	perc_B
Material
Aluminum	2682.564675	729.161699	1229.161699	1724.241277	6777.215	627.833333	255.335333	1090.333333	67000.000000	1106.666667	33.573742	33.573742	32.852516
Brass	2493.752665	666.231954	1166.231954	1661.288758	6777.215	627.833333	199.378000	1095.000000	27166.666667	850.000000	33.669625	33.669625	32.660751
Copper	2517.624408	674.133626	1174.133626	1669.357156	6777.215	627.833333	178.328000	1134.000000	42050.000000	1275.000000	33.646560	33.646560	32.706881
CopperWire	2550.382660	685.108613	1185.108613	1680.165434	6777.215	627.833333	194.312000	1202.000000	10725.000000	2302.500000	33.635408	33.635408	32.729184
Iron	2615.048063	706.744919	1206.744919	1701.558225	6777.215	627.833333	303.478250	1252.000000	38250.000000	1275.000000	33.615036	33.615036	32.769927
PaintedIron	2565.362838	690.110973	1190.110973	1685.140891	6777.215	627.833333	358.354000	1253.000000	12742.857143	1351.428571	33.628394	33.628394	32.743213
StainlessSteel	2654.982309	719.993417	1219.993417	1714.995476	6777.215	627.833333	400.068444	1252.777778	45566.666667	2217.777778	33.590844	33.590844	32.818312

color = ['red','blue','green','orange','cyan','black','yellow']

df_All_Bands = pd.DataFrame(df_plot1.All_Bands)

df_All_Bands.plot(kind='bar', y=0, color=color, legend=False, rot=0, figsize=(10,5))
plt.title('IMAGE WITH ALL CHANNELS',fontsize=20)
plt.grid(True)
plt.xlabel('Material',fontsize=18)
plt.ylabel('Sum of image matrix',fontsize=18)
plt.savefig(folder+"_Sum of image matrix.png")
plt.show()

df_Max_Histog = pd.DataFrame(df_plot1.Max_Histog)

df_Max_Histog.plot(kind='bar', y=0, color=color, legend=False, rot=0, figsize=(10,5))
plt.title('IMAGE WITH ALL CHANNELS-Max_Histog',fontsize=15)
plt.grid(True)
plt.xlabel('Material',fontsize=18)
plt.ylabel('Max_Histog',fontsize=18)
plt.savefig(folder+"_Max_Histog.png")
plt.show()

df_perc = pd.DataFrame(df_plot1.perc_R)

df_perc.plot(kind='bar', y=0, color=color, legend=False, rot=0, figsize=(10,5))
plt.title('IMAGE WITH CHANNELS-Percentage R',fontsize=15)
plt.grid(True)
plt.xlabel('Material',fontsize=18)
plt.ylabel('Idx_Max_Histog',fontsize=18)
plt.show()

loc_Array_sum = np.arange(len(df_plot1.index))+0.1 # Offsetting the tick-label location

loc_r = np.arange(len(df_plot1.index))-0.1 # Offsetting the tick-label location
loc_g = np.arange(len(df_plot1.index))-0.0 # Offsetting the tick-label location
loc_b = np.arange(len(df_plot1.index))+0.1 # Offsetting the tick-label location

#xtick_loc = list(loc_Array_sum) + list(loc_r) + list(loc_g) + list(loc_b)
#xticks = list(selected.keys())+ list(rejected.keys())
colors = ['darkred','red','green','blue','orange','cyan','black','yellow']
plt.figure(figsize=(12,5))

plt.bar(loc_r, df_plot1.perc_R, color='red', width=0.1, label='Band R')
plt.bar(loc_g, df_plot1.perc_G, color='green', width=0.1,label='Band G')
plt.bar(loc_b, df_plot1.perc_B, color='blue', width=0.1,label='Band B')

plt.title('Percentage R,G,B',fontsize=20)
plt.xlabel('Material',fontsize=18)
plt.ylabel('RGB',fontsize=18)
plt.grid(True)
plt.xticks(xtick_loc, xticks, rotation=90)
plt.legend(bbox_to_anchor=(.8,0.8),\
    bbox_transform=plt.gcf().transFigure)
plt.savefig(folder+"_Bar Diagram_perc_RGB.png")
plt.show()

df_Idx_Min_Histog = pd.DataFrame(df_plot1.Idx_Min_Histog)

df_Idx_Min_Histog.plot(kind='bar', y=0, color=color, legend=False, rot=0, figsize=(10,5))
plt.title('IMAGE WITH ALL CHANNELS-Idx_Min_Histog',fontsize=15)
plt.grid(True)
plt.xlabel('Material',fontsize=18)
plt.ylabel('Idx_Min_Histog',fontsize=18)
plt.savefig(folder+"_Idx_Min_Histogram.png")
plt.show()

df_Idx_Min_Histog = pd.DataFrame(df_plot1.Idx_Min_Histog)

df_Idx_Min_Histog.plot(kind='bar', y=0, color=color, legend=False, rot=0, figsize=(10,5))
plt.title('IMAGE WITH ALL CHANNELS-Idx_Min_Histog',fontsize=15)
plt.xlabel('Material',fontsize=18)
plt.ylabel('Idx_Min_Histog',fontsize=18)
plt.show()

loc_Array_sum = np.arange(len(df_plot1.index))
xtick_loc = list(loc_Array_sum)  
xticks = list(df_plot1.index)

df_plot1.plot( y=["All_Bands","Sum_Ch0","Sum_Ch1", "Sum_Ch2","Color_dec","Med_Extrems"],
              figsize=(12,6), grid=True )
plt.xticks(xtick_loc, df_plot1.index, rotation=0)
plt.title('IMAGE WITH ALL CHANNELS',fontsize=20)
plt.xlabel('Material',fontsize=18)
plt.ylabel('Sum of image matrix',fontsize=18)
plt.legend(loc='upper right', ncol=1, fancybox=True, shadow=True,bbox_to_anchor=(1.1, 1.05))
plt.savefig(folder+"_resume all channels.png")
plt.show()

df_plot1

	All_Bands	Sum_Ch0	Sum_Ch1	Sum_Ch2	Color_dec	Med_Extrems	Max_Histog	Idx_Max_Histog	Min_Histog	Idx_Min_Histog	perc_R	perc_G	perc_B
Material
Aluminum	2682.564675	729.161699	1229.161699	1724.241277	6777.215	627.833333	255.335333	1090.333333	67000.000000	1106.666667	33.573742	33.573742	32.852516
Brass	2493.752665	666.231954	1166.231954	1661.288758	6777.215	627.833333	199.378000	1095.000000	27166.666667	850.000000	33.669625	33.669625	32.660751
Copper	2517.624408	674.133626	1174.133626	1669.357156	6777.215	627.833333	178.328000	1134.000000	42050.000000	1275.000000	33.646560	33.646560	32.706881
CopperWire	2550.382660	685.108613	1185.108613	1680.165434	6777.215	627.833333	194.312000	1202.000000	10725.000000	2302.500000	33.635408	33.635408	32.729184
Iron	2615.048063	706.744919	1206.744919	1701.558225	6777.215	627.833333	303.478250	1252.000000	38250.000000	1275.000000	33.615036	33.615036	32.769927
PaintedIron	2565.362838	690.110973	1190.110973	1685.140891	6777.215	627.833333	358.354000	1253.000000	12742.857143	1351.428571	33.628394	33.628394	32.743213
StainlessSteel	2654.982309	719.993417	1219.993417	1714.995476	6777.215	627.833333	400.068444	1252.777778	45566.666667	2217.777778	33.590844	33.590844	32.818312

# Copy dataframe to arrange values
df_plot2 =  df_plot1.copy() 
df_plot2

	All_Bands	Sum_Ch0	Sum_Ch1	Sum_Ch2	Color_dec	Med_Extrems	Max_Histog	Idx_Max_Histog	Min_Histog	Idx_Min_Histog	perc_R	perc_G	perc_B
Material
Aluminum	2682.564675	729.161699	1229.161699	1724.241277	6777.215	627.833333	255.335333	1090.333333	67000.000000	1106.666667	33.573742	33.573742	32.852516
Brass	2493.752665	666.231954	1166.231954	1661.288758	6777.215	627.833333	199.378000	1095.000000	27166.666667	850.000000	33.669625	33.669625	32.660751
Copper	2517.624408	674.133626	1174.133626	1669.357156	6777.215	627.833333	178.328000	1134.000000	42050.000000	1275.000000	33.646560	33.646560	32.706881
CopperWire	2550.382660	685.108613	1185.108613	1680.165434	6777.215	627.833333	194.312000	1202.000000	10725.000000	2302.500000	33.635408	33.635408	32.729184
Iron	2615.048063	706.744919	1206.744919	1701.558225	6777.215	627.833333	303.478250	1252.000000	38250.000000	1275.000000	33.615036	33.615036	32.769927
PaintedIron	2565.362838	690.110973	1190.110973	1685.140891	6777.215	627.833333	358.354000	1253.000000	12742.857143	1351.428571	33.628394	33.628394	32.743213
StainlessSteel	2654.982309	719.993417	1219.993417	1714.995476	6777.215	627.833333	400.068444	1252.777778	45566.666667	2217.777778	33.590844	33.590844	32.818312

df_plot2.Med_Extrems    = df_plot2.Med_Extrems + 2000
df_plot2.Max_Histog     = df_plot2.Max_Histog  + 1500
df_plot2.Idx_Max_Histog = df_plot2.Idx_Max_Histog + 1000
df_plot2.Min_Histog     = df_plot2.Min_Histog + 500
df_plot2.Idx_Min_Histog = df_plot2.Idx_Min_Histog - 1000
df_plot2.head()

	All_Bands	Sum_Ch0	Sum_Ch1	Sum_Ch2	Color_dec	Med_Extrems	Max_Histog	Idx_Max_Histog	Min_Histog	Idx_Min_Histog	perc_R	perc_G	perc_B
Material
Aluminum	2682.564675	729.161699	1229.161699	1724.241277	6777.215	2627.833333	1755.335333	2090.333333	67500.000000	106.666667	33.573742	33.573742	32.852516
Brass	2493.752665	666.231954	1166.231954	1661.288758	6777.215	2627.833333	1699.378000	2095.000000	27666.666667	-150.000000	33.669625	33.669625	32.660751
Copper	2517.624408	674.133626	1174.133626	1669.357156	6777.215	2627.833333	1678.328000	2134.000000	42550.000000	275.000000	33.646560	33.646560	32.706881
CopperWire	2550.382660	685.108613	1185.108613	1680.165434	6777.215	2627.833333	1694.312000	2202.000000	11225.000000	1302.500000	33.635408	33.635408	32.729184
Iron	2615.048063	706.744919	1206.744919	1701.558225	6777.215	2627.833333	1803.478250	2252.000000	38750.000000	275.000000	33.615036	33.615036	32.769927

loc_Array_sum = np.arange(len(df_plot2.index))
xtick_loc = list(loc_Array_sum)  
xticks = list(df_plot1.index)

df_plot2.plot( y=['Color_dec','Med_Extrems', 'Max_Histog', 'Idx_Max_Histog',
                  'Min_Histog','Idx_Min_Histog','perc_R','perc_G','perc_B'],figsize=(12,6), grid=True )
plt.xticks(xtick_loc, df_plot2.index, rotation=0)
plt.title('IMAGE WITH ALL CHANNELS - Color and Histogram',fontsize=18)
plt.xlabel('Material',fontsize=18)
plt.ylabel('Average of Color and Histogram',fontsize=18)
plt.legend(loc='upper right', ncol=1, fancybox=True, shadow=True,bbox_to_anchor=(1.1, 1.05))
plt.savefig(folder+"_color_and_histogram.png")
plt.show()

# Create Xlabels
loc_Array_sum = np.arange(len(df_plot1.index))+0.0 # Offsetting the tick-label location
loc_r = np.arange(len(df_plot1.index))+0.1 # Offsetting the tick-label location
loc_g = np.arange(len(df_plot1.index))-0.0 # Offsetting the tick-label location
loc_b = np.arange(len(df_plot1.index))-0.1 # Offsetting the tick-label location

xtick_loc = list(loc_g)  
xticks = list(df_plot1.index)

# Plot

# Plot  Bar Graph
#df_plot1.plot(kind='bar', figsize=(12,5), grid=True, color='darkred',fontsize=18)
loc_Array_sum = np.arange(len(df_plot1.index))+0.0 # Offsetting the tick-label location
loc_b = np.arange(len(df_plot1.index))+0.1 # Offsetting the tick-label location
loc_g = np.arange(len(df_plot1.index))-0.0 # Offsetting the tick-label location
loc_r = np.arange(len(df_plot1.index))-0.1 # Offsetting the tick-label location

#xtick_loc = list(loc_Array_sum) + list(loc_r) + list(loc_g) + list(loc_b)
#xticks = list(selected.keys())+ list(rejected.keys())
colors = ['darkred','red','green','blue','orange','cyan','black','yellow']
plt.figure(figsize=(12,5))

plt.bar(loc_Array_sum, df_plot1.All_Bands, color=colors[0], width=0.1, label='All_Bands')
plt.bar(loc_r, df_plot1.Sum_Ch0, color=colors[1], width=0.1,label='Band R')
plt.bar(loc_g, df_plot1.Sum_Ch1, color=colors[2], width=0.1,label='Band G')
plt.bar(loc_b, df_plot1.Sum_Ch2, color=colors[3], width=0.1,label='Band B')

plt.title('IMAGE WITH ALL CHANNELS',fontsize=20)
plt.grid(True)
plt.xlabel('Material',fontsize=18)
plt.ylabel('Sum of image matrix',fontsize=18)
plt.xticks(xtick_loc, xticks, rotation=0)
plt.legend(bbox_to_anchor=(.8,0.8),\
    bbox_transform=plt.gcf().transFigure)
plt.savefig(folder+"_all bands.png")
plt.show()

plt.figure(1)
plt.figure(figsize=(17, 4))
plt.tight_layout()
plt.subplot(231)
plt.title('IMAGE CHANNEL 0')
plt.xticks(rotation=45)
plt.grid(True)
plt.plot(df_plot1.Sum_Ch0, 'k--')

plt.subplot(232)
plt.title('IMAGE CHANNEL 1')
plt.xticks(rotation=45)
plt.grid(True)
plt.plot(df_plot1.Sum_Ch1,  'r--')

plt.subplot(233)
plt.title('IMAGE CHANNEL 2')
plt.xticks(rotation=45)
plt.plot(df_plot1.Sum_Ch2,  'g--')
plt.grid(True)
plt.suptitle('Sum Matrix of channels',fontsize=20,y=1.08)
#plt.tight_layout()
plt.subplots_adjust(top=0.8)
plt.savefig(folder+"_Sum Matrix of channels.png")
plt.show()

<Figure size 432x288 with 0 Axes>

# Pèrcentage of R,G,B
plt.figure(1)
plt.figure(figsize=(17, 4))
plt.tight_layout()
plt.subplot(231)
plt.title('IMAGE CHANNEL R')
plt.xticks(rotation=45)
plt.grid(True)
plt.plot(df_plot1.perc_R, 'r--')

plt.subplot(232)
plt.title('IMAGE CHANNEL G')
plt.xticks(rotation=45)
plt.grid(True)
plt.plot(df_plot1.perc_G,  'g--')

plt.subplot(233)
plt.title('IMAGE CHANNEL B')
plt.xticks(rotation=45)
plt.plot(df_plot1.perc_B,  'b--')
plt.grid(True)

plt.suptitle('Percentage of R,G,B',fontsize=20,y=1.08)
#plt.tight_layout()
plt.subplots_adjust(top=0.8)
plt.savefig(folder+'_Percentage_RGB.png', bbox_inches='tight', pad_inches=0.0)
plt.show()

<Figure size 432x288 with 0 Axes>

# Plot channel based
plt.plot(df_plot1.All_Bands)
plt.title('IMAGE WITH ALL CHANNELS',fontsize=20)
plt.xlabel('Material',fontsize=18)
plt.ylabel('Sum of image matrix',fontsize=18)
plt.xticks(rotation=45)
plt.grid(True)
plt.savefig(folder+'_Sum_all_channels.png', bbox_inches='tight', pad_inches=0.0)
plt.show()

# Plot based on color
plt.plot(df_plot1.Color_dec/1000)
plt.title('IMAGE WITH COLORS BASED',fontsize=20)
plt.xlabel('Material',fontsize=18)
plt.ylabel('Average of color image',fontsize=18)
plt.xticks(rotation=45)
plt.grid(True)
plt.show()

# Plot based on Extrems of the Bands
plt.plot(df_plot1.Med_Extrems/10)
plt.title('IMAGE WITH EXTREMS BANDS BASED',fontsize=20)
plt.xlabel('Material',fontsize=18)
plt.ylabel('Average of Extrems image',fontsize=18)
plt.xticks(rotation=45)
plt.grid(True)
plt.savefig(folder+'_color_based.png', bbox_inches='tight', pad_inches=0.0)
plt.show()

Create Histograms

https://www.cambridgeincolour.com/pt-br/tutoriais/histograms1.htm

https://www.cambridgeincolour.com/pt-br/tutoriais/image-noise.htm

http://www2.ic.uff.br/~aconci/aula-2-2015-AI.pdf

https://www.ic.unicamp.br/~ra144681/misc/files/ApostilaProcDeImagesParteI.pdf

histograma, também conhecido como distribuição de frequências ou diagrama das frequências, é a representação gráfica, em colunas (retângulos), de um conjunto de dados previamente tabulado e dividido em classes uniformes.

Histogramas:

O histograma de uma imagem cinza e uma funcao discreta h(l) (vetor) que produz o numero de ocorrencias de cada nıvel de cinza na imagem. O histograma normalizado h(l)/|DI | representa a distribuicao de probabilidade dos valores dos pixels.

Imagens claras possuem histogramas com altas concentracoes de pixels de alto brilho. Imagens escuras possuem histogramas com altas concentracoes de pixels de baixo brilho. O contraste maior esta associado a um grau maior de dispersao do histograma.

No caso de imagens multiespectrais, cada banda e´ requantizada em um certo numero de intervalos, de forma que o espaco de caracterısticas Zk ´e dividido em hipercubos (bins do histograma). A contagem de cores em cada bin ´e usada no c´alculo do histograma. Assim, para cada bin, precisamos analisar os n´ıveis de cinza das 3 bandas da imagem colorida (RGB).

Entendendo Histogramas:

O histograma mostra a frequencia dos valores de brilho da imagem, ou seja, a quantidade de luz presente na imagem.

list_of_images

['Aluminum_1.tif',
 'Aluminum_2.tif',
 'Aluminum_3.tif',
 'Aluminum_4.tif',
 'Aluminum_5.tif',
 'Aluminum_6.tif',
 'Brass_1.tif',
 'Brass_2.tif',
 'Brass_3.tif',
 'Brass_4.tif',
 'Brass_5.tif',
 'Brass_6.tif',
 'CopperWire_1.tif',
 'CopperWire_2.tif',
 'CopperWire_3.tif',
 'CopperWire_4.tif',
 'CopperWire_5.tif',
 'CopperWire_6.tif',
 'CopperWire_7.tif',
 'CopperWire_8.tif',
 'Copper_1.tif',
 'Copper_2.tif',
 'Copper_3.tif',
 'Copper_4.tif',
 'Iron_1.tif',
 'Iron_2.tif',
 'Iron_3.tif',
 'Iron_4.tif',
 'PaintedIron_1.tif',
 'PaintedIron_2.tif',
 'PaintedIron_3.tif',
 'PaintedIron_4.tif',
 'PaintedIron_5.tif',
 'PaintedIron_6.tif',
 'PaintedIron_7.tif',
 'StainlessSteel_1.tif',
 'StainlessSteel_2.tif',
 'StainlessSteel_3.tif',
 'StainlessSteel_4.tif',
 'StainlessSteel_5.tif',
 'StainlessSteel_6.tif',
 'StainlessSteel_7.tif',
 'StainlessSteel_8.tif',
 'StainlessSteel_9.tif']

# Delete values from list - Bad image names
def remove_values_from_list(list_values, mask):
    list_new = list()
    for list_value in list_values:
        if(mask not in list_value):
            print(list_value)
            list_new.append(list_value)
    return list_new 

# Remove from list names with 'MASK'
new_list = remove_values_from_list(list_of_images, 'MASK')

Aluminum_1.tif
Aluminum_2.tif
Aluminum_3.tif
Aluminum_4.tif
Aluminum_5.tif
Aluminum_6.tif
Brass_1.tif
Brass_2.tif
Brass_3.tif
Brass_4.tif
Brass_5.tif
Brass_6.tif
CopperWire_1.tif
CopperWire_2.tif
CopperWire_3.tif
CopperWire_4.tif
CopperWire_5.tif
CopperWire_6.tif
CopperWire_7.tif
CopperWire_8.tif
Copper_1.tif
Copper_2.tif
Copper_3.tif
Copper_4.tif
Iron_1.tif
Iron_2.tif
Iron_3.tif
Iron_4.tif
PaintedIron_1.tif
PaintedIron_2.tif
PaintedIron_3.tif
PaintedIron_4.tif
PaintedIron_5.tif
PaintedIron_6.tif
PaintedIron_7.tif
StainlessSteel_1.tif
StainlessSteel_2.tif
StainlessSteel_3.tif
StainlessSteel_4.tif
StainlessSteel_5.tif
StainlessSteel_6.tif
StainlessSteel_7.tif
StainlessSteel_8.tif
StainlessSteel_9.tif

# Remove from list names with 'Enh'
new_list = remove_values_from_list(new_list, 'Enh')

Aluminum_1.tif
Aluminum_2.tif
Aluminum_3.tif
Aluminum_4.tif
Aluminum_5.tif
Aluminum_6.tif
Brass_1.tif
Brass_2.tif
Brass_3.tif
Brass_4.tif
Brass_5.tif
Brass_6.tif
CopperWire_1.tif
CopperWire_2.tif
CopperWire_3.tif
CopperWire_4.tif
CopperWire_5.tif
CopperWire_6.tif
CopperWire_7.tif
CopperWire_8.tif
Copper_1.tif
Copper_2.tif
Copper_3.tif
Copper_4.tif
Iron_1.tif
Iron_2.tif
Iron_3.tif
Iron_4.tif
PaintedIron_1.tif
PaintedIron_2.tif
PaintedIron_3.tif
PaintedIron_4.tif
PaintedIron_5.tif
PaintedIron_6.tif
PaintedIron_7.tif
StainlessSteel_1.tif
StainlessSteel_2.tif
StainlessSteel_3.tif
StainlessSteel_4.tif
StainlessSteel_5.tif
StainlessSteel_6.tif
StainlessSteel_7.tif
StainlessSteel_8.tif
StainlessSteel_9.tif

list_of_images = new_list

path = mypath + '/' + folder + '/'
path

'C:\\Users\\manuel.robalinho\\Google Drive\\UPT_Portucalense\\Trabalho final\\Classificacao_Sucata\\Jupyter_Notebook/imagedata06/'

# HISTOGRAMS
# Print Histograms for all folder images
# list_of_images has all the name files

for x in list_of_images:
    print('Cv2 Histogram for File:', x)
    print_cv_hist(path, x)

Cv2 Histogram for File: Aluminum_1.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Aluminum_1.tif

Cv2 Histogram for File: Aluminum_2.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Aluminum_2.tif

Cv2 Histogram for File: Aluminum_3.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Aluminum_3.tif

Cv2 Histogram for File: Aluminum_4.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Aluminum_4.tif

Cv2 Histogram for File: Aluminum_5.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Aluminum_5.tif

Cv2 Histogram for File: Aluminum_6.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Aluminum_6.tif

Cv2 Histogram for File: Brass_1.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Brass_1.tif

Cv2 Histogram for File: Brass_2.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Brass_2.tif

Cv2 Histogram for File: Brass_3.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Brass_3.tif

Cv2 Histogram for File: Brass_4.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Brass_4.tif

Cv2 Histogram for File: Brass_5.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Brass_5.tif

Cv2 Histogram for File: Brass_6.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Brass_6.tif

Cv2 Histogram for File: CopperWire_1.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/CopperWire_1.tif

Cv2 Histogram for File: CopperWire_2.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/CopperWire_2.tif

Cv2 Histogram for File: CopperWire_3.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/CopperWire_3.tif

Cv2 Histogram for File: CopperWire_4.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/CopperWire_4.tif

Cv2 Histogram for File: CopperWire_5.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/CopperWire_5.tif

Cv2 Histogram for File: CopperWire_6.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/CopperWire_6.tif

Cv2 Histogram for File: CopperWire_7.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/CopperWire_7.tif

Cv2 Histogram for File: CopperWire_8.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/CopperWire_8.tif

Cv2 Histogram for File: Copper_1.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Copper_1.tif

Cv2 Histogram for File: Copper_2.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Copper_2.tif

Cv2 Histogram for File: Copper_3.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Copper_3.tif

Cv2 Histogram for File: Copper_4.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Copper_4.tif

Cv2 Histogram for File: Iron_1.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Iron_1.tif

Cv2 Histogram for File: Iron_2.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Iron_2.tif

Cv2 Histogram for File: Iron_3.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Iron_3.tif

Cv2 Histogram for File: Iron_4.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Iron_4.tif

Cv2 Histogram for File: PaintedIron_1.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/PaintedIron_1.tif

Cv2 Histogram for File: PaintedIron_2.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/PaintedIron_2.tif

Cv2 Histogram for File: PaintedIron_3.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/PaintedIron_3.tif

Cv2 Histogram for File: PaintedIron_4.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/PaintedIron_4.tif

Cv2 Histogram for File: PaintedIron_5.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/PaintedIron_5.tif

Cv2 Histogram for File: PaintedIron_6.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/PaintedIron_6.tif

Cv2 Histogram for File: PaintedIron_7.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/PaintedIron_7.tif

Cv2 Histogram for File: StainlessSteel_1.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_1.tif

Cv2 Histogram for File: StainlessSteel_2.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_2.tif

Cv2 Histogram for File: StainlessSteel_3.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_3.tif

Cv2 Histogram for File: StainlessSteel_4.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_4.tif

Cv2 Histogram for File: StainlessSteel_5.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_5.tif

Cv2 Histogram for File: StainlessSteel_6.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_6.tif

Cv2 Histogram for File: StainlessSteel_7.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_7.tif

Cv2 Histogram for File: StainlessSteel_8.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_8.tif

Cv2 Histogram for File: StainlessSteel_9.tif
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_9.tif

# HISTOGRAMS
# Print Histograms for all folder images
# list_of_images has all the name files

for x in list_of_images:
    print('Matplot Histogram for File:', x)
    print_matplot_hist(path, x)

Matplot Histogram for File: Aluminum_1.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Aluminum_1.tif

Matplot Histogram for File: Aluminum_2.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Aluminum_2.tif

Matplot Histogram for File: Aluminum_3.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Aluminum_3.tif

Matplot Histogram for File: Aluminum_4.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Aluminum_4.tif

Matplot Histogram for File: Aluminum_5.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Aluminum_5.tif

Matplot Histogram for File: Aluminum_6.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Aluminum_6.tif

Matplot Histogram for File: Brass_1.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Brass_1.tif

Matplot Histogram for File: Brass_2.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Brass_2.tif

Matplot Histogram for File: Brass_3.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Brass_3.tif

Matplot Histogram for File: Brass_4.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Brass_4.tif

Matplot Histogram for File: Brass_5.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Brass_5.tif

Matplot Histogram for File: Brass_6.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Brass_6.tif

Matplot Histogram for File: CopperWire_1.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/CopperWire_1.tif

Matplot Histogram for File: CopperWire_2.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/CopperWire_2.tif

Matplot Histogram for File: CopperWire_3.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/CopperWire_3.tif

Matplot Histogram for File: CopperWire_4.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/CopperWire_4.tif

Matplot Histogram for File: CopperWire_5.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/CopperWire_5.tif

Matplot Histogram for File: CopperWire_6.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/CopperWire_6.tif

Matplot Histogram for File: CopperWire_7.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/CopperWire_7.tif

Matplot Histogram for File: CopperWire_8.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/CopperWire_8.tif

Matplot Histogram for File: Copper_1.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Copper_1.tif

Matplot Histogram for File: Copper_2.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Copper_2.tif

Matplot Histogram for File: Copper_3.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Copper_3.tif

Matplot Histogram for File: Copper_4.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Copper_4.tif

Matplot Histogram for File: Iron_1.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Iron_1.tif

Matplot Histogram for File: Iron_2.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Iron_2.tif

Matplot Histogram for File: Iron_3.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Iron_3.tif

Matplot Histogram for File: Iron_4.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/Iron_4.tif

Matplot Histogram for File: PaintedIron_1.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/PaintedIron_1.tif

Matplot Histogram for File: PaintedIron_2.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/PaintedIron_2.tif

Matplot Histogram for File: PaintedIron_3.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/PaintedIron_3.tif

Matplot Histogram for File: PaintedIron_4.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/PaintedIron_4.tif

Matplot Histogram for File: PaintedIron_5.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/PaintedIron_5.tif

Matplot Histogram for File: PaintedIron_6.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/PaintedIron_6.tif

Matplot Histogram for File: PaintedIron_7.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/PaintedIron_7.tif

Matplot Histogram for File: StainlessSteel_1.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_1.tif

Matplot Histogram for File: StainlessSteel_2.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_2.tif

Matplot Histogram for File: StainlessSteel_3.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_3.tif

Matplot Histogram for File: StainlessSteel_4.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_4.tif

Matplot Histogram for File: StainlessSteel_5.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_5.tif

Matplot Histogram for File: StainlessSteel_6.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_6.tif

Matplot Histogram for File: StainlessSteel_7.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_7.tif

Matplot Histogram for File: StainlessSteel_8.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_8.tif

Matplot Histogram for File: StainlessSteel_9.tif
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata06/StainlessSteel_9.tif

print('Finished: ',folder )

Finished:  imagedata06